Gunfire Location and Surveillance
System
Group 3
Denis Alvarado BSCpE
Zayd Babamir BSEE
Christian Kon BSEE
Luis Salazar BSCpE
TABLE OF CONTENTS
1. Executive Summary __________________________________________________1
2. Project Description ___________________________________________________ 2
2.1 Objectives ______________________________________________________________ 2
2.2 Requirements ___________________________________________________________ 3
2.3 Specifications ___________________________________________________________ 3
3. Research ___________________________________________________________ 5
3.1 Existing Products________________________________________________________ 5
3.1.1 Thales ______________________________________________________________________ 5
3.1.2 ShotSpotter __________________________________________________________________ 6
3.1.3 SWATS _____________________________________________________________________ 8
3.1.4 Boomerang __________________________________________________________________ 9
3.1.5 SENTRI ____________________________________________________________________ 10
3.2 Multilateration _________________________________________________________ 11
3.2.1 2D Multilateration ____________________________________________________________ 11
3.2.2 3D Multilateration ____________________________________________________________ 14
3.3 Triangulation __________________________________________________________ 15
3.3.1 2D Triangulation _____________________________________________________________ 17
3.3.2 3D Triangulation _____________________________________________________________ 18
3.4 Signal Reproduction _____________________________________________________ 22
3.5 Gunshot Acoustic Properties ______________________________________________ 24
3.6 Wavelet vs Fourier ______________________________________________________ 38
3.7 Initial Hardware Choices_________________________________________________ 44
3.1.1 Main Board Design ___________________________________________________________ 44
3.1.2 Processor ___________________________________________________________________ 46
3.1.3 Memory/RAM _______________________________________________________________ 47
3.1.4 Bluetooth ___________________________________________________________________ 49
3.1.5 GPS _______________________________________________________________________ 50
3.1.6 Backup Power Battery Size _____________________________________________________ 51
3.1.7 Backup Battery Power Souce ___________________________________________________ 54
4. Hardware Design ___________________________________________________ 57
4.1 Initial Embedded Board Design ___________________________________________ 57
4.1.1 BeagleBone Black Microcontroller Model _________________________________________ 57
4.1.2 Processor ___________________________________________________________________ 59
4.1.3 RAM Module Configuration ____________________________________________________ 66
4.1.4 DC Power Configuration _______________________________________________________ 70
4.1.5 Peripheral Inputs/Outputs ______________________________________________________ 71
4.2 Initial Sound Capture Subsystem __________________________________________ 76
4.2.1 Microphone _________________________________________________________________ 76
4.2.2 Microphone Array ____________________________________________________________ 80
4.2.3 Amplifier ___________________________________________________________________ 81
4.2.4 Analog to Digital Converter ____________________________________________________ 85
4.2.5 FPGA______________________________________________________________________ 90
i
4.2.6 DPSRAM Audio Buffer _______________________________________________________ 92
4.3 Initial Power Subsystem _________________________________________________ 97
4.3.1 Primary Power _______________________________________________________________ 97
4.3.2 Secondary Power _____________________________________________________________ 97
4.3.3 Power Switching _____________________________________________________________ 99
4.4 Current Hardware Design _______________________________________________ 100
4.4.1 Audio Capture ______________________________________________________________ 101
4.4.2 Data Processing _____________________________________________________________ 101
4.4.3 Power _____________________________________________________________________ 102
5. Software Design ___________________________________________________ 103
5.1 Initial Embedded Board Design __________________________________________ 103
5.2 Linux ________________________________________________________________ 106
5.3 Gunshot Recognition Algorithm __________________________________________ 106
5.4 Location Algorithm ____________________________________________________ 107
6. Project Prototype Construction ______________________________________ 112
6.1 Hardware ____________________________________________________________ 112
6.1.1 Initial Design _______________________________________________________________ 112
6.1.2 Current Design _____________________________________________________________ 113
6.2 Software______________________________________________________________ 113
7. Fabrication and Testing _____________________________________________ 114
7.1 Hardware ____________________________________________________________ 114
7.1.1 Initial Design _______________________________________________________________ 114
7.1.2 Current Design _____________________________________________________________ 115
7.2 Software______________________________________________________________ 116
7.2.1 Initial Design _______________________________________________________________ 116
7.2.2 Current Design _____________________________________________________________ 117
8. Design Summary and Conclusion _____________________________________ 118
9. Administration Content _____________________________________________ 119
9.1 Budget and Funding ____________________________________________________ 119
9.2 Planning and Milestones ________________________________________________ 120
9.3 Management Style _____________________________________________________ 121
9.4 Division of Labour _____________________________________________________ 123
Acknoweldgements ________________________________________________________ 124
Appendix A ______________________________________________________________ 125
Appendix B ______________________________________________________________ 128
ii
Chapter 1
Executive Summary
With the recent high profile mass shootings that have flooded the news, firearm
safety and response has unfortunately become a pressing issue and serious
subject of public safety. No matter the political standpoint of any individual, both
pro- and anti-gun advocates can agree that any new technology to make a
firefight safer and shorter for innocent bystanders is a good implementation.
Currently, banks and many government entities are equipped with panic buttons
that employees can trigger when danger arises. Although these are useful tools,
the human factor is still present in triggering that alarm, and there is room for lifethreatening instances in that time.
Furthermore, not all entities, institutions, or businesses have such systems in
place making themselves more vulnerable to gun crime. Additionally, alarms can
provide only limited information to incoming law enforcement agents. The
location of a threat provides a better picture of events as they unfold. Lastly, on
many occasions where video recording is not in use, forensic investigators have
the difficult task of calculating and recording where they believe all gunshots
were fired after a crime. These current issues motivated our team of engineers
to create the Gunshot Location And Surveillance System, GLASS.
GLASS addresses these issues by providing accurate detailed information of gun
fire events in a timely manner, as they occur. In the case of a power outage,
GLASS is solar powered, with a backup battery to provide energy grid
independence. On receiving a gunfire signal, GLASS triangulates the source’s
position using triangulation, characterizes the firearm being used, and then alerts
a separate device with the pertinent information.
.
1
Chapter 2
Project Description
2.1 Objectives
GLASS (Gunfire Location and Surveillance System) is a modular security alarm
system that is designed to greatly increase the safety of innocents during a
criminal shooting. GLASS is also a self-sustaining unit that generates and stores
its own power source in case of particular, emergency situations.
In the primary module, GLASS monitors for the specific audio traces that are
unique to gunshots and immediately sends an alarm to local law enforcement
and institution security (if applicable). Although bystanders may still call for
emergency help, GLASS provides an almost instantaneous response to alert
authorities promoting swifter law enforcement arrival and bystander safety.
The secondary module of GLASS triangulates at least the relative position, if not
the exact position of where the firearm(s) is(are) discharged and records the
position on a digital schematic of the institution in which it is installed.
Considering that the microphones utilized in GLASS may not be installed in every
room of an institution, the system provides a relative position of the gunshots if a
shooting were to occur in a room not fully equipped with GLASS microphones.
GLASS also records a timestamp associated with each location where a round is
being fired.
Considering that GLASS is a modular system, one where features can easily be
added, it may perform other functions. These functions include real-time updates
to mobile devices with gunfire locations and depending on whether or not an
institution has electronic door locking mechanisms, the possibility of GLASS
“leading” a criminal to a predesignated area away from innocent bystanders by
locking and unlocking particular doors.
On the internal level, GLASS is self-sustaining both in power and
implementation. In case of power failure to the institution, GLASS powers itself
during the day with photovoltaic solar panels which also charges a backup
battery for the evening time. The battery can be integrated into the institutions
power grid to guarantee a full charge at all times. GLASS is also designed to be
“hands-free” in terms of implementation. Once configured and installed, GLASS
requires no additional interaction from the institution implementing it. This selfsustainability makes GLASS easy to utilize, free to power, and secure.
2
2.2 Requirements
For utilization of at least the internal, first, and secondary levels of GLASS the
following are the minimum requirements:
●
●
●
●
Four microphones for monitoring and location triangulation
Data processing unit for audio input and alarm/location marking output
Digital schematic of room where GLASS is installed
Secondary Android enabled device to receive communications from
GLASS
● Photovoltaic solar panel
● Battery backup
● Audio, phone, and power wiring
2.3 Specifications
Through the speakers, GLASS constantly monitors the audio signals for three
conditions to be met which are specific to gunshots recording continuously to a
buffer. The first condition that needs to be met is a decibel level. After an
adequate amount of time a portion of the buffer is sent to the processing unit to
process and check the recorded data for all gunfire remaining gunfire conditions.
All firearms (except those equipped with suppressors) produce a level of 140 dB
when fired. The general range for a gunshot discharge is between 140-180 dB.
Once this first condition is met, most other sound originators are ruled out out but
GLASS checks two more conditions. The second audible trace GLASS monitors
for is the peak frequency caused by the gunpowder explosion within the chamber
of the firearm. This second condition allows GLASS to record what caliber
weapon is being fired since different calibers produce specific peak frequencies.
The last condition the audio record must meet is proof of the subsonic frequency
the round makes as it flies through the air. The last condition guarantees that an
actual firearm was discharged not a recording.
Once all three audio conditions are met, GLASS automatically either sends an
emergency message or calls law enforcement with information detailing the
location of shooting and the details recorded, and calculated by the system..
Glass then uses sound recognition software to match the weapon type.
As the emergency call is being made, GLASS continues tracking and recording
all locations and time instants where a firearm is discharged. This positioning is
calculated by a triangulation method utilizing at least three microphones. The
three microphones record the analog signal where it is then converted into a
digital signal which the data processing unit will run the GLASS triangulation
program to locate the gunshot relative to the particular GLASS node.
3
The data processing unit is comprised of an ARM processor mounted on a
custom designed board running a Linux OS with a GLASS software interface, a
data storage drive, a power supply, integrated memory, and a network adapter
for communication. Each microphone has its own single core microprocessor to
quickly analyze and process the initial discharge. This data is then forwarded to
the main processing unit described above for locations processing and mapping.
Area schematics are preloaded into the system where GLASS tracks gunfire
locations and timestamps on digital copies.
4
Chapter 3
Research
3.1 Existing Products
Several products similar to what we are trying to do already exist in the
marketplace, but they are very expensive and limited to the public. The US Army
is has been the pioneer in this invention. The use of gunfire locator in recently
wars where US has been involved has saved many lives already. For that
reason, we think is good idea to implement this invention and make it affordable
for most of the population. Similar, some police stations already count with this
device and its use has contributed to response and arrives to crime scenes
faster; as a result, the number of people killed has decreased. Again the main
purpose of GLASS is to detect shooting and save lives. Some of products that
detect gunshots are described as follows:
3.1.1 – THALES
THALES Gunshot Detector is one of the products in the market. This is an
advanced solution with the same purpose as our idea to locate a shooting as
quickly as possible and save lives. But again its market is limited to police
stations. This product is designed to detect and localize shots from non-military
weapon in large urban areas in combinations with architecture systems that uses
surveillance cameras. It works with acoustical signal detection for noise or quiet
location deployments.
When a firearm is being shot, this device collects the sound and compares it with
a central data. If the sound matches with any of the data, the sensor sends the
location to the nearest police station and record the event with video system and
CAD system as well. As a major in security technologies, Thales has put together
integrated solutions to tackle part of the problems with shooting with events, but
the price for this device is not affordable for everyone. The table 3.1 shows its
specifications.
THALES Gunshot Detector specifications
Dimensions Weight
Operating
Humidity
temperature
Operating
voltage
Power
consumption
185x100x8
5 mm
-25°c
55°c
12 VCD
4 watts
4 Kg
to 20%-80%
Table 3.1 - THALES Gunshot Detector specifications
5
Fig. 3.1 - THALES Gunshot Detector
3.1.2 – ShotSpotter
ShotSpotter is another product that analyze solutions and alert gunfire. They
claim agencies that have adopted this device and best practices as part of a
comprehensive violence in urban areas a crime has been reduced. Again this
device is used in police and law enforcement agencies. When ShotSpotter is in
action, it provides real time information about gunfire and explosions, enabling a
more effective response to the nearest police station giving them a complete
picture of the crime, so they can have better idea of what is going on to better
protect their personnel and their communities. This device is more sophisticated
than the previous one. It gives immediate alert, even when no one calls 911,
precise location anywhere within coverage area including latitude/longitude and
street address, exact time and number of rounds fired, shooter position, speed
and direction of travel if moves.
Also with ShotSpotter data is possible to help yield critical forensic data after the
crime, which includes: sequence of rounds fired with time and position data, type
or types of weapons used, tactical or sting operations, number of weapons or
shooters, and weapons cyclic rates.
The feature built-in ShotSpotter solves interoperating by using standards-based
communications protocols to share data across systems, roles and other
agencies for a streamlined and coordinated response to all critical events.
ShotSpotter can interface with video surveillance systems that require guidance
to train individual cameras and capture video intelligence at the scene of an
incident and its surroundings.
To make it more affordable, ShotSpotter has created four implementations; Table
3.2 shows general ShotSpotter specifications.
6
Shot Spotter Flex, it is the most powerful and cheaper effective gunfire alert and
analysis service available that provides comprehensive, real time gunshot
detection and location for any safety agency. ShotSpotter Flex delivers all the
critical incident and forensic data agencies need to do their jobs more effectively:
● Real-time alerts for outdoor gunfire in coverage areas
● Precise location anywhere within the coverage area including
●
●
●
●
latitude/longitude and street address
Direction and speed of travel of one or more shooters
Exact time of each shot and potential number of shooters
Comprehensive database of incident history and data
Round-by-round post-incident forensic analysis to support investigators
and prosecutors
ShotSpotter Onsite, it is designed for agencies that require more control of onpremise solutions and delivers the same intelligence, response, safety and
advantages including:
● Real-time alerts for outdoor gunfire in coverage areas
● Precise location anywhere within the coverage area including
●
●
●
●
latitude/longitude and street address
Direction and speed of travel of one or more shooters
Exact time of each shot and potential number of shooters
Comprehensive database of incident history and data
Round-by-round post-incident forensic analysis to support investigators
and prosecutors
ShotSpotter SpecialOps, it gives the ability to check targeted areas for short-term
and temporary operations to proactively enhance security against possible
threats and allow quick reaction to crimes. It uses pre-loaded software and
wireless sensors to allow simplified setup for coverage of small areas. It is
designed to enhance protection for:
●
●
●
●
●
VIP and dignitary events
Special event security
Tactical or sting operations
Area security
Serial or active shooter scenarios
ShotSpotter Security, it is the most complex of the four systems. It capable of
protecting buildings, borders and other public and private infrastructures from
terrorist or criminal attacks. It provides alerts security personnel to attacks from
firearms and explosions instantly and allows them to take intelligent actions
immediately. As the other four systems, it can be configured to integrate with
7
video surveillance system and enhance their functionality. Key benefits of
ShotSpotter Security include:
●
●
●
●
Real-time delivery of precise, geo-referenced incident alerts
Instantly makes incident audio and data available to personnel
Interoperable with critical public safety and security technologies
Capable of alerting multiple agencies for coordinated response
ShotSpotter
Application
framework
Display
Silverlight
XGS
SXGS
Operating
system
& Microsoft
Processor
Memory
Internet
bandwidth
1.6 GHz
1 GB
256 kbps
Table 3.2 - ShotSpotter Gunshot Detector specifications
Fig. 3.2 - ShotSpotter Gunshot Detector
3.1.3 – SWATS
Shoulder-Worm Acoustic Targeting System. A total of 17,000 systems have been
sold to US Army alone since 2008, where it is known as the “Individual Gunshot
Detector” It is also used by the Marine Corps. It weighs only 300-grams and its
design shoulder-carried sensor pad contains the microphone, a GPS receiver, a
magnetic compass, a gyro, and accelerometers. It is capable of giving accuracy
of ±7.5° in azimuth for a maximum declared range of 400 meters in open area
and provides the soldier with the relative position of the gunshot source alone
with recording the grids in the system to update the relative position while the
other soldiers move around and share this information with the rest of squad. All
this information can be viewed through an aural device or display screen unit.
The specification of this device are shown on table 3.3.
8
SWATS
Dimensions Weight
Operating
Humidity
temperature
Storage
temperatur
e
3x3x0.75 in
-20°c
60°c
-20°c
70°c
1 lb
to 5%-95%
Power
consumptio
n
to 1 watt
Table 3.3 - SWATS Gunshot Detector specifications
Fig. 3.3 - SWATS Gunshot Detector
3.1.4 – Boomerang
Boomerang is another shotgun detector that detects the origin of shooter, and it
is available for US military, law enforcement agencies, municipalities, and other
approved US domestic and foreign entities. Currently is employed by US forces
in Iraq and Afghanistan. It works with passive acoustical detection and computerbased signal processing to locate the gunshot and when is attached to a vehicle
operates either stationary or moving. It uses a single mast-mounted compacted
array of microphones to detect incoming fire.
Boomerang indicates the azimuth of incoming small-arms fire by actuating a light
to show the clock direction, and Boomerang announces that direction using a
recorded voice. Boomerang indicates the range and elevation on an LED screen
display. The lighted displays can be dimmed. The following table 3.4 shows
Boomerang’s specifications.
9
Boomerang
Dimensions
Weight
Operating
Storage
Operating Power
temperature temperature voltage
consumptio
n
7.25Wx4.75H
x
3.25D in
15 lb
0°c to 50°c
-40°c
71°c
to 9-30 V dc
25 watts
Table 3.4 - Boomerang Gunshot Detector specifications
Fig. 3.4 - Boomerang Gunshot Detector
3.1.5 – SENTRI
SENTRI, It is a product developed by Safety Dynamics specialized in the use of
small sensor to recognize and locate threats, and it is currently selling and
supporting to law enforcements agencies. The system recognizes gunshots and
expositions and sends the signal to cameras which can locate the source of the
event. The patent developed by the laboratory of Neural Dynamics at the
University of Southern California is the core of the acoustic recognition capability
and is based on neurobiological principles of brain signal processing similar to
human brain. It is capable of recognizing an acoustical signal even in the
presence of high noise. SENTRI is part of a network of surveillance cameras
which listen for gunshot and provides to police stations with the ability to use its
audio during crime scenes. Table 3.5 shows SENTRI’s specifications.
10
SENTRI
Dimensions Memory
8.25x4.25x
1.25 in
Operating
Operating
temperature frequency
16
MB 0°c to 70°c
SDRAM
225 Mhz
Operating
voltage
Sampling
rate
+12,-12, +5 500
Khz
V
per
channel
Table 3.5 - SENTRI Gunshot Detector specifications
Fig. 3.5 - SENTRI Gunshot Detector
3.2 Multilateration
To have a better accuracy to show the coordinates where the shotgun event
occurs, the multilateration has been broken into two sections: 2D multilateration
and 3D multilateration. Each section has its own particularity and is explained as
follows.
3.2.1 - 2D Multilateration
To find the location of the source there are different ways to approach the
possible arms fire event. Multilateration, which needs only one array with at least
three microphones to solve for the two dimensional case and at least four
microphones in order to calculate in three dimensions, allows GLASS to solve
this task with only one processor. This method uses hyperbolic multilateration
over triangulation which is easier to implement since the array can be arranged in
any manner.
To calculate the possible location of the sound source, hyperbola are created by
relating the magnitude of the distance vectors each microphone. These
calculations include the time difference of arrival of a sound wave between two
11
microphones. See Figure 3.6a. other points on the hyperbola are theoretical
locations that must be made additional relations to eliminate them from the realm
of possibilities.
For example, when the sound wave reaches the two microphones at the same
time, the resultant is a straight line. This line represents the possible locations of
the source given that the arrival to both microphones is equal.
By relating a third microphone three different hyperbolas of possibilities based on
their respective differences in time of arrival. The Intersection of these lines, as
seen in Figure 3.6, corresponds to the location of the sound itself.
Regardless of the position of the microphones, three microphones will always
produce hyperboloids with a singular intersection point. If they do not intersect, a
solution is impossible. In the real world this may become a problem as
measurement inaccuracies, or even the inaccuracies caused by taking a poor
sampling of the actual audio may cause the hyperboloids to not intersect.
a)
Hyperbole - 2 microphones
b) Hyperbole - 3 microphones
Figure 3.6 - 2D Hyperbolic Multilateration
Ignoring the possibility of an unsolvable relation, the above intersection
represents the location of the sound source. Since the location of each node is
known that the time of installation, and the position for each microphone is of
equal distance from the node’s center, each microphones position is known The
relative location of the source and the exact locations of the microphones can be
used to calculate the exact location of the sound source. To begin we must
calculate the speed of sound for the given temperature where the speed of
sound, C, temperature, T in Celsius.
C(T)=331 3√1
T
273 15
12
The distance from a particular microphone to the source of the sound can be
calculated with the equation below. Where The t represents the time it takes for
the sound wave produced by the gunshot to propagate to the particular
microphone.
Di =C T xti
The distance can be represented by the the magnitude of the distance vector that
is drawn from the sound source to the microphone. In two dimensions where are
three microphones located at points A, B, and C, and have a given x-coordinate
and a y-coordinate. For the sound source, we denote its position in space as the
variables x, and y, the strategy is then to solve for this point.
√ x xA
2
y yA
C x tA
√ x xB
2
y yB
C x tB
√ x xC
2
y yC
C x tC
Now these equations require us to know the time it too for the sound to
propagate to each microphone. This is an unobtainable values at this point as the
time of the gunshot event is not known. However we can relate the magnitude of
any two vectors together by realizing that their magnitudes should be equivalent
with the only difference being the difference of arrival between the nodes and if
we multiply by the spread of sound, we get the difference in the distance from the
sound source to the microphones. the difference in time of arrival to each
microphone can be easily be found by determining when the maximum value
occurs for each microphone.
1 C√ x xB
2
y yB
√ x xA
2
y yA
tB tA
tAB
1 C√ x xC
2
y yC
√ x xA
2
y yA
tC tA
tAC
For this example, we can simplify the mathematics by setting the origin to point
A. This leaves us with two equations and two unknowns which is sufficient to
determine an answer.
1 C√ x xB
2
y yB
√x2
y
tB tA
tAB
1 C√ x xC
2
y yC
√x2
y
tC tA
tAC
13
3.2.2 - 3D Multilateration
When considering the three dimensional case we start similarly to the two
dimensional case however the hyperboloids are three dimensional such as in
figure 3.7. naturally four hyperboloids will be required to solve for this case so
another microphone is necessary. Care must be kept when placing the
microphones, as they may not lie on the same plane. If They do, the resulting
solution will have multiple solutions. For this reason, we place them at equal
distance from the center of the node on right angles from each other
Figure 3.7 – Possible location with a half hyperboloid
The equations for the magnitudes of the distance vectors are the same in three
dimensions except that the z component of the vector must also be integrated to
the equations.The addition of the fourth microphone also incorporates the third
equation listed below.
1 C√ x xB
2
y yB
z zB
1 C√ x xC
2
y yC
z zC
1 C√ x xD
2
y yD
z zD
√x2
y
z2
tB tA
tAB
2
√x2
y
z2
tC tA
tAC
2
√x2
y
z2
tD tA
tAD
14
The use of multilateration to find the sound location has also carries some
possible errors. Any discrepancies due to the position of the microphone or
timing as it relates to the arrival to each microphone can cause the system of
equation to become unsolvable.
3.3 Triangulation
Triangulation uses the fundamentals of euclidean trigonometry to determine the
position of an object. Given that the speed of sound is constant for a given
temperature, the delay between two nodes receiving the same sound can
thereby be determined. Then, by incorporating another node, a direction can be
obtained the resulting vector is sufficient to place the origin of the sound.
Every time a firearm is shot generates two distinct impulse sounds: the muzzle
blast and the shockwave. See figure 3.8. The muzzle blast is the result of the
rapid discharge of the propellant and the fast combustion generated when the
unburned part of the propellant mixes with air outside the muzzle, and the origin
of this impulse is measured from the weapon right after the shot. In the other
hand, the shock wave is created by the trajectory of the bullet travelling through
the air, similar to the waves created by aircraft during a flight.
From the weapon to the microphone array, the muzzle blast wave originates from
a point source (the muzzle), and it propagates spherically from its origin at the
speed of sound. For that reason, it can be detected from anywhere around the
firing position. The muzzle blast wave propagation is directly toward the
microphone array without any obstructions during the path.
Similar to the triangular wave formed by a boat on the surface of the water, the
shock wave creates a cone. The tip of the cones travels along the line of fire at
the speed of the bullet, but the acoustic wave from that propagates perpendicular
to the shock wave front at the speed of sound. See figure 3.8.
Fig. 3.8- Impulse sounds at low angle of fire
15
From the microphone array perception, the bullet travels from the muzzle of the
weapon to the point where the shockwave detaches from the bullet and
continues its propagation in direction to the microphone array. The time that the
shock wave takes to reach the microphone array is calculated by adding the
bullet’s time of travel to the point of detachment and the propagation time of the
shockwave from the point of detachment to the microphone array.
When the angle of fire is high, the point of detachment of the shock wave occurs
very close to the weapon; as a result, the shockwave and the muzzle blast
appear to originate from the same area. See figure 3.9. So the arrival time and
the direction of the arrival for both the muzzle and the shock wave are
considered the same. In our project, we will assume a high angle of fire;
therefore, only the shockwave will be used to calculate the distance from the
weapon to the microphone array.
Fig. 3.9- Impulse sounds at high angle of fire
With the shockwave and time difference to reach each microphone in the array, it
is possible to predict the entire geometry of the fire event by collecting and
analyzing the sound, which is the only source of information available for our
shotgun detector. An idealized shockwave and the muzzle blast waves received
by the microphone array are shown of figure 3.10. The first wave is the shock
wave followed by the muzzle blast. That is exactly what happens when a firearm
is shot; you will hear the shockwave before the muzzle blast and look in the
direction of the detach point and confuse it with the origin of the firing point,
which also coincide with the muzzle blast that arrives later.
16
Fig. 3.10- Idealized sound received by a microphone
3.3.1 2D Triangulation
2-dimensional triangulation is done through the use of two microphone nodes
each containing three microphones.
Some assumptions have been made during this experiment. If the microphones
are close enough and the sound source is far away, we can assume the sound
wave approaches as a straight line perpendicular to the line of origin. So the
distance Δx can be found, see figure 3.11.
Delta x as a function of the speed of sound and time b and time a.
Δx1 =C(T)×( tb -ta )
The speed of sound as a function of temperature:
C(T)=331 3√1
T
273 15
The angle θ1 can be found using trigonometry since we know the distance Δx,
the side of the array, and S. Similar by relating the angles α1and θ1, the angle
α1can be found after θ1. See figure 3.11.
Δx1
θ1 cos 1
s
α1 θ1 −30°
Δy1 =C(T)×( tc -tb )
Δy1 =C(T) x cos(180°−60°−θ1 )
Angles β1, β2, and β3 of the larger triangle can be found also since they are
related with α1 and α2. See figure 3.11.
17
To establish a relationship between the angles β and α, we first need to know the
orientation of each array with respect to the line that connects the two arrays.
Since each array of microphones will have a compass on it, this information will
be identified. According to figure 3.11 below, if both arrays of microphones are
oriented in the same direction and their bases are parallel to the line that
connects both arrays of microphones (L), then we would be able to use the
following set of equations.
β1 =90°+α1
β2 =90°+α2
β3 =180°−(β1 β2 )
To determine D, law of sines is used:
sinβ2 / L = sinβ3 / D
If we know the exact location of each array, we can find the exact source sound
location. The information collected by the GPS solved this problem. We assume
that each size array is very small in comparison to the distance from the sound
source to the arrays; therefore, the GPS can identify anywhere inside the array
and still be accurate it. In other words, each microphone is located at the same
distance away as seen it by the GPS unit.
3.3.2 3D Triangulation
To find the coordinates of the sound source, we will add to the vertical coordinate
of the GPS to the vertical portion of the distance and the horizontal coordinate of
the GPS from the first array to the horizontal portion of the distance. Both
horizontal and vertical coordinates will point to either direction North, South, East,
and West after being normalized. All this data will be acquired from the compass
on the array; therefore, the angles α and β will be adjusted accordingly. The set
of equations used to find the horizontal and vertical components of the portion of
the distance (D) are showing below based on figure 3.11. In this case, we
assumed that the positive vertical direction will be North, and the positive
horizontal direction will point to East.
vertical component
sin(180°−β1 ) = vertical / D
horizontal component
cos(180°−β1 ) = horiz / D
After combining these equations, each array would produce a single equation for
the angle that represents with only variable variables ta, tb, the temperature, T,
and α.
18
α1 θ1 −30°
but we know
Δx1
θ1 cos 1
s
Δx1 =C(T)×( tb -ta )
C(T)=331 3√1
T
273 15
so after plug in these variables into the equation α1
α1 =cos−1(C(T)×( tb1 -ta1 )/S)−30°
similar α2 can be calculated
α2 =cos−1(C(T)×( tb2 -ta2 )/S)−30°
θ1 −30°
The previous equations can be combined to obtain a single equation that will
resolve the distance D between the sound source and the first array.
D=(sinβ2 ×L) / sinβ3
but we know:
β1 =90°+α1
β2 =90°−α2
β3 =180°−(β1 β2 )
after substituting these β1 , β2 , and β3 into the equation D=(sinβ2 ×L) /
sinβ3 , the distance D can be calculated
D=(sin(90°−α2 )×L) / ( sin (180°− ( (90°+α1 )+(90°−α2 ) ) )
19
Figure 3.11 – 2D triangulation
Similar to the two dimensional triangulation, we need to generate a set of
equations for three dimensional triangulation. The process is the exactly the
same except that for each array will have two angles instead of one. Each array
will form a pyramid, and the two directions generated by each array will lead us
to find the exact sound location.
Hence for this case, we need to consider that the sound travels on a surface as a
plane instead of single line, and for the two angles from the each array two
additional perspectives are required. See figure 3.12. In this figure, a side view is
rotated 30˚ above being parallel with the ground; therefore, the side view is
perpendicular to the plane made by the front face. The dot line in the center
represents the rear microphone that would be recessed into the page.
The top view is perpendicular to the plane that is the ground and the dot in the
center represents the top microphone which would be protruding out of the page.
This top view is rotated from the side view such that the line connecting the two
lower microphones is fixed and the upper microphone is rotated down and out of
the page by 60˚.
The same formula used in two dimensional case is used in the three dimensional
case:
Δx1 =C(T)×( tb -ta )
20
C is then dependent on the angle:
a view =C(T)×sinα1
b view =C(T)×sinα2
θ1 is found as with the two dimensional case:
θ1 =cos−1(Δx1 /s)
α1 θ1 −30°
For the purposes of GLASS, this scheme may be used to eliminate multiple
sound signatures from the whole system. However using multiple nodes
provides a distinct problem under real life situations. If GLASS were to depend
on multiple nodes, the system may fail for various conditions. With the failure of a
singular node, be it through mechanical failure, or insufficient signal strength to
reach both nodes, it is impossible to determine the source’s location.
Figure 3.12 – 3D triangulation
21
3.4 Signal Reproduction
The main purpose of this project is to find the exact gunshot location. The sound
waves originated by this sound would then be reproduced all the way through
microphones into GLASS. But have you asked yourself how microphones work?
Perhaps you had hear all types of microphones: studio, PA, boom, instruments,
boundary, headset, etc. There is a very good reason for this diversity of
microphones even if they have the same basic function, but need to be adapted
to wide variety of uses and environments.
All microphones have a common function to convert a sound wave into an
electrical signal that is then transformed as voltage and current that can be
analyzed with measurement instruments. To perform this task each microphone
has a skinny membrane, diaphragm, which is similar to human ear. See figure
3.13. The process is simple, when the sound waves reach a microphone’s
diaphragm, they cause it to move within an electromagnetic field that creates a
some electrical current and then transmitted to output devices, which reproduces
the original sound wave and reinforce it.
Figure 3.13 – cross section of microphone’s diaphragm
Most of the microphones used for audio systems fall into three basic designs
which are often used to organize microphones into distinct categories: dynamic,
condenser and ribbon. The connection between the microphone’s diaphragm and
the output device can be wired or wireless. A microphone is wired when is
physically connected by a cable to the output. Wireless microphones use both a
transmitter and compatible receiver.
22
There are several types of microphone pickup patterns. Here we included four of
the most common: unidirectional or cardioids, bidirectional, omnidirectional and
switchable. See figure 3.14. In our design we will use omnidirectional microphone
since its patterns are sensitive to sound from all directions.
● Unidirectional or cardioid pickup patterns are most sensitive to sound
produced on the front side of the microphone capsule
● Bidirectional pickup patterns are sensitive to signals emanating from the
front and back sides of the microphone capsule while rejecting sounds
from the left and right sides of the microphone capsule.
● Omnidirectional or boundary pickup patterns are sensitive to sound from
all directions of the microphone capsule.
● Switchable pickup patterns are hybrid microphones that can be switched
from one pickup pattern to another for all-in-one flexibility in different
environments.
Figure 3.14 – Microphone directional patterns
We chose the omnidirectional microphone because is capable of collecting
sounds equally from any direction and will deliver the most accurate environment
representation which includes capturing room resonance along with the source.
This type of microphone is perfect to be used on open areas, but it also has a
drawback since its sensibility to feedback need proper placement in a live setting.
Perhaps the greatest challenge that GLASS will face is to differentiate gunshot
sounds from other sounds. A gunshot sound has a particular characteristic that
use and explosive charge to propel the bullet out of the barrel, and the sound
generated travels in all directions. This energy travels in the same direction the
barrel is pointed as well. During the fire shot, a shock wave called muzzle blast is
emitted, and this what the analog to digital device will be detecting to locate the
origin of the gunshot.
23
3.5 Gunshot Acoustic Properties
To build sensing algorithms that can detect a gunshot by sound, the nature of
gunshots must first be understood. A gunshot has a few properties that make it a
unique sound. For one, gunshots are extremely loud; there is seldom a gun
(even small caliber guns) that has a noise level less than 130 dB. This loud
“bang” is the noise associated with the actual combustion of the propellant within
the firearm’s cartridge. Essentially an explosion occurs within the firing chamber
of the gun producing this loud noise. A secondary noise is created after the
firearm discharge in which the bullet travel produces a sonic “crack” that is due to
the fast rate of travel of the actual projectile. In this section some time and
frequency analysis is done on a few audio samples of gunshots in order to
explore the acoustic qualities of various types of firearms. The first firearm sound
sample examined is a Ruger LCR:
Ruger LCR
Firearm Type
Revolver
Caliber
.38 Special
Barrel Length
1.875”
Construction Materials
Polymer fire control, aluminum frame,
steel cylinder
Figure 3.15 - Ruger LCR .38 Special
The audio sample was taken in an open area but with a large hill at the end of
the shooting area as a backstop. The sample contains the recording of one
single discharge of the Ruger LCR. Of note is that revolvers are notorious for the
loud gunshots due to there being a gap between the cylinder in which the
cartridge is contained and the actual frame of the gun, as opposed to traditional
pistols in which the “breech” of the firearm is all enclosed form the environment.
As such in the time domain signal of the gunshot an increase in amplitude can be
24
seen after the initial “spike” due to the propellant discharge, that indicates an
echo that came back to the microphone.
Figure 3.16 - Time domain of Ruger LCR sample
A frequency analysis is then viewed:
Figure 3.17 - Frequency domain of Ruger LCR sample
25
The peak frequency is located at 568 Hz, initial research indicated that the peak
frequency would be somewhere in the 300 - 1000 Hz range, holding true for the
Ruger LCR. Interestingly, removing the echo from the sample reveals the impact
it has on the frequency content of the sample:
Figure 3.18 - Ruger LCR with echo removed from sample
The frequency content remains mostly unchanged except in the peak to 1200 Hz
range and the peak frequency is close to the original one with a value of 574 Hz.
This is indicative that the content of the echo should have little impact on the
analysis of the firearm discharge. Next a semi-automatic pistol with a closed
breech is sampled.
Glock 19
Firearm Type
Semi-Automatic Pistol
Caliber
9x19mm Parabellum
Barrel Length
4.01”
Construction Materials
Polymer frame, metal slide
Figure 3.19 - Glock 19
26
The Glock 19 has a closed breech being a semi-automatic pistol, compared to
the Ruger LCR revolver. The audio sample was captured in a large open field
where there is not any surface for the gunshot wave to bounce off of and cause
echo.
Figure 3.20 - Time domain of Glock 19 sample
The impulse followed by the quick die down is apparent with little echo occurring
in this sample. The Glock 19 had a similar recording setup as the Ruger LCR,
however, from the time domain waveform it does not look as though it reached
the same loudness as the revolver as expected because of it’s closed breech.
3.21 - Frequency domain of Glock 19 sample
27
The frequency analysis shows that the peak occurs at 393 Hz. Interestingly,
compared to the Ruger LCR, the G19 has much less frequency content in the 0
to peak frequency range with a much higher slope between the two. This is
believed to be because of the closed breech design of the G19. To solidify the
difference between the revolver and semi-automatic pistol, an additional semiautomatic pistol is analyzed.
Colt M1911
Firearm Type
Semi-Automatic Pistol
Caliber
.45 Automatic Colt Pistol
Barrel Length
5.03”
Construction Materials
Steel
Figure 3.22 - Colt M1911
The Colt 1911 is one of the earliest semi-automatic pistols ever made, first
manufactured in the year of it’s namesake, 1911. However it remains popular to
this day only having recently been ousted from the US military in favor of the
Beretta M9. This gun is similar to the G19 in that it is a semi-automatic pistol but
is otherwise very different being that it has an all steel construction and a longer
barrel. From the previous analyses, the thought is that the closed breech of the
semi-automatic firearms cut out the low frequency components of the firearms
discharge.
28
Figure 3.23 - Time domain go 1911 sample
The time domain signal shows that the sample has a bit of an echo contained in
it that propagates itself back to the microphone about .05 seconds after the shot.
Figure 3.24 - Frequency domain of 1911 sample
Interestingly, the hypothesis that closed breech semi-automatic pistols lack low
frequency content beneath their peak seems to be even truer with the 1911 than
the G19. The peak frequency occurs at 643 Hz, still in the 300-1000 Hz range
expected. The handguns evaluated so far all share in common that they utilize
rather powerful cartridges. The .22 LR caliber cartridge is one of the most
popular cartridges and one of the most used in gun related crime.
The following analysis compares this rather small and “weak” cartridge to the
29
larger caliber pistols tested above, and then will be tested to a .22 LR rifle to
establish a difference between pistols and rifles with the cartridge being equal.
Ruger SR22
Firearm Type
Semi-Automatic Pistol
Caliber
.22 LR
Barrel Length
3.5”
Construction Materials
Polymer frame and steel slide
Figure 3.25 - Time domain of SR22 sample
30
The Ruger SR22 sample shows a some substantial echo but as shown earlier
this is not believed to affect the frequency content much.
Figure 3.26 - Frequency domain of SR22 sample
The SR22 frequency content seems well in line with the other semi automatic
pistols sampled with a peak frequency of 534 Hz. Of note though is the rate at
which the high frequency components fall off with the SR22.
The SR22 will compare well with it’s rifle cousin. One of the most popular .22 LR
rifles is the Ruger 10/22.
Ruger 10/22
Firearm Type
Semi-Automatic Rifle
Caliber
.22 LR
Barrel Length
18.5”
Construction Materials
Steel
Figure 3.27 - Ruger 10/22
31
Figure 3.28 - Time domain Ruger 10/22 sample
In the 10/22, the peak is not as distinguished as it was with the pistols. Whether
this is a trait among rifles will be further tested momentarily. This particular
sample does have a good deal of echo so a frequency analysis both containing
and not containing the echo will be done to understand the effect on the
frequency content.
Figure 3.29 - Frequency domain of 10/22 with echo
32
With the echo the sample immediately distinguishes itself from the handguns with
a peak frequency of 1335 Hz, a good deal higher than that of the three firearms
tested thus far. This is a promising find that can possibly be a defining
characteristic of rifle frequency content.
Figure 3.30 - Frequency domain of 10/22 without echo
With the echo cut from the sample, the peak frequency remains the same at
1335 Hz, this is a satisfactory result indicating that like the handguns the
presence of echoes may be a non-deciding factor in frequency analysis. To
understand how a rifle differentiates itself from a handgun further, a rifle utilizing
a medium sized rifle round will be used in an effort to make a fair comparison
between the larger handguns and this rifle.
ArmaLite AR15
Firearm Type
Semi-Automatic Rifle
Caliber
.223 Remington
Barrel Length
20”
Construction Materials
Composite frame, steel barrel
Figure 3.31 - AR15
33
Figure 3.32 - Time domain of AR15 sample
The time domain figure most definitely bears a resemblance more to the 10/22
than any of the handguns. This could perhaps be attributed to the rather long
barrels of the rifles that extend the duration of the gunfire sound. the correlation
in time domain is promising.
Figure 3.33 - Frequency of AR15 sample
34
The frequency analysis however does not bear the similarities hoped for. Like the
handguns, the peak frequency is in the first 1 KHz band at 617 Hz. The
frequency content of this sample shows quite a similarity to the handguns tested
earlier. Given that the cartridges of the AR15 are not too far in size from the
handguns tested, naturally the next firearm to sample is a large caliber rifle.
Springfield M1903
Firearm Type
Rifle
Caliber
.30-06
Barrel Length
20”
Construction Materials
Wood frame, steel barrel
Figure 3.34 - M1903
Figure 3.35 - Time domain of M1903 sample
35
The time domain of the Springfield shows more resonant sound after the
discharge than the pistols but not quite to the extent of the other rifles featured.
For reference, the round this firearm is shooting is about three times the size of
that in the AR-15. The frequency analysis reveals a new figure:
Figure 3.36 - Frequency domain of M1903 sample
The peak frequency is a low 271 Hz and similar to the AR-15, a smooth increase
in frequency up to the peak frequency is observed. Seeing a trend in the caliber
and peak frequency, the last firearm to be tested is a 12 gauge shotgun.
Although shotgun cartridges are measured in gauge, the effective caliber of a 12
gauge shotgun is .72 which is substantially larger than the biggest round tested
so far.
Remington 870
Firearm Type
Pump Action Shotgun
Caliber
12 Gauge
Barrel Length
24”
Construction Materials
Steel
Figure 3.37 - Remington 870
36
Figure 3.38 - Time domain of 870 sample
The 870 time domain waveform is akin to the other firearms with long barrels
analyzed so far. The distinction is clear that long barrel firearms have a longer
period of high amplitude sound following the initial cartridge combustion.
Figure 3.39 - Frequency domain of 870 sample
In the frequency analysis, a very low frequency was expected, however the peak
frequency occurs at 454 Hz. Like the other long barrel firearms analyzed so far,
the 870 exhibits a good deal of ramping before and after the peak frequency.
Overall, this data will help to establish trends in firearm sound so that algorithms
can be developed to distinguish between different types or calibers of firearms.
37
3.6 Wavelet over Fourier
Although the natural tendency when analyzing a signal is to do the Fourier
transform for Glass’ purposes, the wavelet transform gives more pertinent
information. By using this transform we can determine not only what spectral
components exist but also the region in time that those components correspond
to. This makes the wavelet transform a natural choice when determining whether
a gunshot has occurred and the kind of weapon used, as the relative position in
time of the spectral components would remain the same regardless of the
reduction in amplitude due to the decay of the sound envelope.
To decide what will be the best choice for our project, we first need to study the
characteristics of each of the Fourier Transform and the wavelet. Perhaps the
main difference between both of them is that from a wavelet transform both the
time and frequency can be derived at the same time whereas from the Fourier
transform only the frequency is possible to obtain. If the purpose of the project is
to include the time besides the frequency for analyzing a signal, the employ of
Fourier Transform is not convenient.
The basic idea of using Fourier Transform is to break a signal into series of
waves which represent frequencies. There are different applications of the
Fourier Transform; perhaps the sound is the most common. When we hear a
sound, we do not perceive anything else but a bunch of frequencies bouncing
forth and back without notice the actual movement of the molecules on the air.
The Fourier Transform is capable of transforming that sound into waves which
are easier to study and transform them into digital signals. Image processing is
another application of Fourier Transform that converts the impulse response of a
linear filter into the frequency response of the filter. The figure 3.40 shows how
this filter attenuates high frequencies and passes the low frequencies.
38
Figure 3.40– Frequency response of a filter
With the Fourier Transform, it is possible to remove undesirable frequencies. For
instance, the existence of low frequencies on a continuous surface can slowly
unstable the image whereas high frequencies can reproduce the edges of the
image quicker. See figure 3.41. An image could be considered as twodimensional signal that does not change quickly over small distances, so the
change on the image for high frequencies are not entirely visible.
Figure 3.41– Image reproduction
39
Through the Fourier Transform, it is possible to make measurements with the
bandwidth and evaluate each component of the frequency which requires certain
amount of time; as a result, there is no control over the time when the signal was
originated. This is the drawback on analyzing signals with Fourier Transform; it
offers unconditional precision on frequency but worthless on temporal spread of
the signal. To be valid the Fourier Transform, the measurement needs to be
done at a preset time to get the precision on the amplitude of the signal, but null
information about the spectrum of the signal. In our project, we want to
reconstruct the maximum amplitude of the signal so we can distinguish which
event that has passed through the band-pass filter.
After a signal has been reconstructed from a series of samples values, it is easier
to use the Fourier Transform to duplicate it by a superposition of a series of sines
and cosines waves. The following example, figure 3.42, shows how a series of
sine waveforms take an approximation of a square signal after being
reconstructed. That is exactly what the Fourier Transform does. It duplicates the
sample values of the signal by superposition of a series of sine and cosine
waves.
Figure 3.42 – sine waveform representing a square signal
Contrary to Fourier Transform, wavelets that takes place on fixed parameter and
the resulting information is about the temporary extend of the signal and the
spectrum as well. So we can derive both characteristics of the signal: time and
frequency. For that reason, we chose a wavelet to analyze the signal since
localize waves whose energy is concentrated in time and space.
40
Wavelets are better used than Fourier analysis for our project, because they are
used for non-periodic waveforms, and they are also ideal at representing sharp
peaked functions, such as the characteristic of a gunshot. The figure 3.43 show
the difference between wave and wavelet. In the wavelet transform we do not
lose the time information, which is useful in many contexts. Here are some of the
advantages of using wavelets:
● They offer a simultaneous localization in time and frequency
domain.
● With fast wavelet transform, it is possible to do computation very
fast.
● Wavelets have the great advantage of being able to separate the
fine details in a signal. Very small wavelets can be used to isolate
very fine details in a signal, while very large wavelets can identify
coarse details.
● A wavelet transform can be used to decompose a signal into
component wavelets.
● Wavelet theory is capable of revealing aspects of data that other
signal analysis techniques miss the aspects like trends, breakdown
points, and discontinuities in higher derivatives and self-similarity.
● It can often compress or de-noise a signal without appreciable
degradation.
Wavelets are powerful tool which can be used for a wide range of applications
replacing the conventional Fourier Transform. There are different types of
wavelet transform, but we are interested in Discrete Wavelet Transform because
is easy to implement and fast to compute with minimum resources. And for our
project, we chose the Daubechies wavelet function since is similar in shape to a
gunshot. See figure 3.44.
41
.
Figure 3.44 – Common wavelet functions
Wavelets are a prevailing statistical tool which can be used for a wide range of
applications, including:
● Signal processing
● Data compression
● Smoothing and image denoising
● Fingerprint verification Biology for cell membrane recognition, to
distinguish the normal from the pathological membranes
● DNA analysis, protein analysis
● Speech recognition
● Computer graphics and multifractal analysis
With the use of wavelets, it is possible to reconstruct a full signal with a portion of
the original signal information, which makes that data so small to be copied into a
storage device. The compression method has to be done in a way such that the
structure of the signal is kept. That is the advantage that some government
branches have taken, including the FBI. Part of FBI job is to storage tons of
fingerprints, but with the use of wavelets this amount of fingerprints has been
considerable reduced.
In DWT, the most prominent information in the signal appears in high amplitudes
and the less prominent information appears in very low amplitudes. Data
compression can be achieved by discarding these low amplitudes. The wavelet
transforms enables high compression ratios with good quality of reconstruction.
42
At present, the application of wavelets for image compression is one the hottest
areas of research.
The figure 3.45 shows the steps to process a signal using DWT that involve
compression, encoding, denoising, etc. First the signal is either stored or
transmitted including quantization and entropy coding for most compression
applications. The coefficients that are below certain level are discharged and
then replaced with zeros during the reconstruction at the other end. To
reconstruct the signal back, the entropy coding is decoded, quantized, and finally
inverse wavelet transform.
Figure 3.45 – Signal application using Wavelet Transform
Similar to filters used to process signal functions, wavelets can be realized by
iteration of filters with rescaling. The resolution of the signal along with the scale
can be done by doing some filtering and sampling operations. The DWT is
computed by successive low pass and high pass filtering of the discrete time
domain signal. This process is called the Mallat-tree decomposition and its
importance resides on how it connects the continuous time multi resolution to
discrete time filters. The signal denoted by the sequence x[n] , n is an integer.
The low pass filter is denoted by G0 and the high pass filter denoted by H0. At
each stage level, the high pass filter produces detail information d[n] and low
pass filters which are associated with scaling functions produce coarse
approximations a[n].
At each decomposition level, only half of the frequency band is produced by half
band filters; as a result, the frequency resolution is double as uncertainty in
frequency reduced by half. In other words, from the down sampling only one of
the two data is used in this process. This is exactly what Nyquist’s sample set, if
the original signal has a highest frequency w, which requires a sampling
frequency of 2w, then it will have a highest frequency of w/2 radians. It can be
sampled at a frequency of w radians without losing any information even
discarding half the samples. This decimation by 2 halves the time resolution as
the entire signal is now represented by only half the number of samples. What
this process really does is to remove half of the frequencies and the resolution,
but the decimal by 2 double the scale.
To reconstruct this signal back to original, it is good practice to associate time
resolution with high frequencies and frequency resolution with low frequencies.
The reverse filtering process depends on the length of the signal. The DWT of
the original signal is then obtained by all the coefficients, a[n[ and d[n], first
initiate with the very last level of decomposition until reaches the desire level.
43
The reconstruction process really is the reverse of the decomposition process.
The coefficients at every level are upsampled by two on low and high pass
synthesis filters and then added together. The process stops until reaches the
same number of levels as the decomposition process. Similar to decomposition,
the Mallat algorithm works perfectly if both the analysis filters G0 and H0 are
exchanged with the synthesis filters G1 and H1.
3.7 Initial Hardware Choices
In order for GLASS to achieve its goals special attention was paid to the
hardware requirements of the system. Glass is composed of a main board where
an embedded computer running Linux processes data and sends out alerts as is
deemed necessary, a GPS module, a Bluetooth transmitter and an Audio capture
system. In this chapter GLASS’ GPS, Bluetooth and mainboard are detailed as to
their hardware specifications and the choices made.
3.7.1 - Main Board Design
There were many different printed circuit board designs to model the GLASS
main board off of. With the broad types and amount of data needed to be
processed in such short periods of time, there were quite a few parameters to
take into consideration when searching for a development board to model the
GLASS design after. The different models currently in production that were
considered for GLASS are the Raspberry Pi, Pandaboard ES, and Beagleboard
BeagleBone Black.
The Raspberry Pi device was considered first for GLASS due to its small physical
size, is low power, and low cost. There were many specifications that the
Raspberry Pi devices have which originally made it a promising choice for
GLASS. The physical dimensions of the board measure 8.6cm x 5.4cm x 1.5cm
making it ideal for GLASS’ portable design goal. The Raspberry Pi runs a Linux
operating system with customizable embedded software options. Lastly, the
Raspberry Pi does have the peripherals GLASS wished to utilize. Unfortunately,
with further research, the Raspberry Pi lost its place within GLASS project
because it simply was not broad enough a tool to base the GLASS circuit board
design. The input pins are limited and not capable of receiving the amount of
audio signals from the microphone array necessary for gunshot triangulation, the
processor and RAM were much too slow for the data processing speeds needed,
and the overall design would not accept additional RAM.
The next device considered was the Pandaboard ES. Compared to the
Raspberry Pi, the Pandaboard ES is powerhouse in performance. The
Pandaboard ES utilizes the dual-core 1.2 GHz ARM® ARM® Cortex™A9
processor, 1 GB of DDR2 RAM, full 1080p video encoding/decoding, and
multiple expansion headers. Although the Pandaboard ES did have the correct
processor needed for GLASS, as well as the necessary peripherals, the design
44
was much too complex and many of the design features would have been
wasted in GLASS, left unutilized. Also, the Pandaboard ES is quintuple the price
of a Raspberry Pi and was deemed too expensive for the GLASS project’s
budget.
The last device considered for the GLASS project was the Beagleboard
BeagleBone Black board. The BeagleBone Black is the perfect midway point
between the Raspberry Pi and Pandaboard ES for the GLASS project. The
BeagleBone Black has a high performance, low power 1.0 GHz ARM® Cortex-A8
processor, 512 MB DDR3 RAM, Linux compatibility, USB host, small in physical
size, and bluetooth capability. The BeagleBone Black board design was chosen
to be the base model for the GLASS custom printed circuit board with a few
modifications for optimization and customization to the GLASS project. It is
important to note that there is no out-of-the-box solution for the GLASS project
and the BeagleBone Black was chosen simply as a “best fit” for modification.
Below is a table of the microcontroller model specs that was used to determine
the PCB design model.
Raspberry
B
Board Name
Processor
Pi
PandaBoard ES
ARM1176JZ-F
ARM®
A9
BeagleBone Black
Cortex™- Sitara
ARM®,
Cortex™A8
Processor Speed 700 MHz
1.2 GHz
1.0GHz
Processor Cores
1
2
1
RAM Size
512
SDRAM
1 GB DDR2
512 MB DDR3
Audio Input Pins
None
Expandable Pins
Expandable Pins
USB Ports
2
3
2
Ethernet
1
1
2
Bluetooth
Expandable
Yes
Yes
Memory Card
SD
SD
SD
Physical Size
8.6cm x 5.4cm
11.4cm x 10.2cm
256k x 8
Power
2.5 W
3.8 W
2.3 W
Cost
$35.00
$182.00
$45.00
MB
Table 3.6- Microcontroller design model choices.
45
3.7.2 - Processor
There were simply two factors in the decision of which processor to use in the
GLASS project: performance and cost. In an imaginary world of unlimited
budget, the top of the line processor could have been chosen, but realistically
cost plays a monumental role in part purchasing. Five different processors were
considered for the GLASS project. These processors and their specifications are
shown below in Table 3.7.
Part #
AM3358
ZCZD72
AM3358BZ
CZ100
AM3715C
BC100
MCIMX6D5EY MCIMX6Q5EY
M10AC
M10AC
Series
Sitara
ARM®,
Sitara
Cortex™ ARM®,
A8
Cortex™A8
Sitara
ARM®,
Cortex™A
8
ARM®
Cortex™-A9
ARM®
Cortex™-A9
Speed
720MHz
1.2GHz
1.0GHz
1.0GHz
Cores
Single
Core 32 Single Core Single
Dual-Core
bit
32 bit
Core 32 bit bit
RAM
Size
64k x 8
64k x 8
64k x 8
256k x 8
256k x 8
Cost
39.6
41.66
43.75
48.37
60.44
1.0GHz
32 Quad-Core 32
bit
Table 3.7- Processor choices. Green column represents processor in GLASS.
As seen in Table 3.7 above, the five choices for processor are similar but have
distinct differences. The consideration for so many different ARM® Cortex™A8
processors was due to the fact that it was decided to utilize the BeagleBone
Black board design and it has an ARM® Cortex™A8. With careful consideration
and calculation, it was decided that an ARM® Cortex™A9 would be necessary
for GLASS because of its multi-core feature and larger RAM size. The decision
came between the final two processors whose only difference were dual-core vs
quad-core. In the end, it was decided to use the dual-core ARM® Cortex™A9
because it would be able to handle the data processing necessary in GLASS.
The theoretical increase in performance between the quad-core and dual core
ARM® Cortex™A9s could not be justified by the 25% cost increase between the
two processors.
46
3.7.3 - Memory/RAM
Many different types of RAM were considered to be utilized in the GLASS
hardware design. For main processing RAM, there were two choices: DDR2 and
DDR3 SDRAM; and for buffering the data from each microphone/analog to digital
converter including SDRAM, VRAM, and Dual-ported SRAM.
For the main RAM, the ARM® Cortex™A9 only supports DDR2 or DDR3
SDRAM so the choices were few. DDR2 was researched and considered a
strong possibility at first. The basic functionality of DDR2 and DDR3 was similar
enough that DDR2 seemed to be the better choice simply because of its lower
cost. That was until the transfers per cycle numbers were found. The main
difference between DDR2 and DDR3 SDRAM is that DDR2 only has four data
transfers per cycle compared to DDR3’s eight transfers per cycle. The fact that
this difference in the RAM simply doubled the performance, it was clear that the
slight increase in price (under 15%) was worth a 100% increase in performance.
Therefore, DDR3 is the main RAM component in the GLASS hardware design.
RAM Type
DDR2
DDR3
Voltage (V)
1.8
1.5
Maximum Operating Speed (MHz) 1066
2133
Max Transfer Rate (MB/s)
8533
17067
Prefetch Buffer (Bits)
4
8
Cost
Slightly Lower
Slightly Higher
Table 3.8- SDRAM Choices. Green column represents RAM in GLASS.
While already researching DDR2 and DDR3, SDRAM was considered for the
data buffering after each analog to digital converter. With careful consideration
and the advice of a professor, it was concluded that SDRAM used to buffer the
large amount of audio data would create an information bottleneck and would
greatly decrease device performance. The microcontroller would either spend all
of its time retrieving information from the ADC or, if an FPGA handled the data
retrieval, the memory unit would only have a small time frame with the data
written before the FPGA would need to access it again.
At this point, it was decided, dual-ported memory had to be used. Dual-port
memory is necessary for the GLASS data buffering because it allows multiple
reads or writes to occur simultaneously. The most popular dual-ported memory
is VRAM and so it was considered next. VRAM allows the high traffic of write
and reads necessary for the project data processing, but it is dynamic which is
not ideal for signal processing. Lastly, the discarding of VRAM introduced the
final and decided upon buffering memory: Dual-port Static Random Access
47
Memory (DPRAM or DPSRAM). This memory provides the stable buffering
necessary for signal processing while allowing the processor to access the
memory while data is still being written to the memory at the same time.
48
3.7.4 - Bluetooth
GLASS uses Bluetooth as a method of communication between the system and
the a user. Bluetooth was selected because GLASS is intended to work as a
Node based solution so the information can be relayed to a centralized location.
for our purposes, Bluetooth allows glass to accomplish this behavior without the
power drain required for longer distance communication. GLASS also does not
need to relay a great deal of information and even the 1M bit/second of Bluetooth
version 1.2 is sufficient for GLASS’ communication purposes.
The ENW-89841A3KF module was chosen for its low cost, fast transfer rate, and
connectivity through serial connectivity. Additionally the higher output power
gives glass a longer range. The specifications for the ENW-89841A3KF can be
found on the next page. The module connects to a USB port on the main board
that the micro controller may access at will.
Consideration was placed on other forms of communication with the micro. Serial
interface was a strong contender; however the cost for a serial Bluetooth
transceiver far outweighed its usefulness for GLASS. Also GLASS does not need
to transmit large amounts of data so the ENW-89841A3KF is more than
sufficient.
Bluetooth modules
Parameter
Receiver
(1% PER)
BT820
Sensitivity - 89 dBm
ENW-89841A3KF
- 93 dBm
Data Rate
3 Mbps
2178 kpbs
Output Power
8 dBm
10.5 dBm
Operating
Voltage:
Frequency
Supply 1.7 V to 3.6 V
2.402 GHz to 2.48 GHz
1.7 V to 4.8 V
2402 MHz to 2480 MHz
Operating Temperature -30°C to + 85°C
Range
-40°C to + 85°C
Interface type
I2C, PCM, UART
I2S, PCM Audio, USB
Price
14.75
13.55
Table 3.9- Viable Bluetooth transceivers and their statistics
49
3.7.5 - GPS
GLASS Primarily utilizes the onboard GPS as a method of determining the time
of a gunshot like event. However, on startup and periodically, GLASS identifies
its position polling the GPS satellites. Used in conjunction with the relative
locational data obtained through the processing of the sound signals, GLASS
determines the position of a gunshot event.
The Microcontroller interfaces with the GPS through the UART port. This is
sufficient for GLASS because the GPS need only be accessed once per gun shot
occurrence and once on initialization of the system as a whole. Also when glass
accesses the GPS, it must be able to do so asynchronously so the
microcontroller me access the time at its leisure.
The A2200-A in particular offers a balance of virtually all attributes. Seen in Table
3.8 it has a fast time to first start, this decreases the time until GLASS has booted
as the GPS is accessed on booting to retrieve its location. It may not have a
particularly high sensitivity, however the A2200-A UART compatibility makes it
more viable than the A2235-H and since the serial interface is unnecessary the
price and first start time make it a better choice than the GYSFFMAXB.
GPS modules
A2235-H
A2200-A
GYSFFMAXB
Frequency Band
1.575 GHz
1.575 GHz
1.575 GHz
Channels
48
48
88
35s
42s
Time
Start
To
First 35s
sensitivity
- 163 dBm
- 148 dBm
- 146 dBm
Horizontal
Position
Accuracy
2.5m
2.5m
2m
Interface
I2C
I2C UART
13.60
14.42
COST
Serial, UART
16.45
Table 3.10- Viable GPS modules and their statistics
50
3.7.6 - Backup Power Battery Size
Since GLASS utilizes a backup power source, particularly a solar charged
battery, it was necessary to decide how long the GLASS battery would need to
stay charged in the event where the main power is lost and GLASS is needed
for gunshot location and alarming. It was decided that GLASS would self sustain
via battery power alone for at least 12 hours. 12 hours was chosen because if
the main power is cut at the end of daylight, 12 hours of battery charge would
sustain GLASS until sunrise the next morning where the solar cells can start
recharging the battery. Also, considering the nature of a gunshot emergency,
attention is drawn to an area after a gunshot and therefore a power provider
would be close behind to restore power to an area.
Once 12 hours was decided upon as the minimum runtime necessary for the
backup battery to sustain, the overall power consumption of the GLASS device
had to be calculated. The table below uses simply Ohm’s Law to calculate the
power consumption of the major components of GLASS. Major components for
this table’s sake are defined as core components within GLASS that are
essential to the function of the project and/or has the highest in power
consumption. The “Misc” section is an estimation of the smaller, less power
dependent components all added together. The MISC line is estimated due to
the lack of clear power consumption listed in the component datasheets, and the
sheer number of passive components that are utilized in GLASS. The values are
considered theoretical because the voltage and current values were derived from
data sheets of each component, typically under a section titled “Under Maximum
Load.”
Voltage
(V)
Current (A)
#
Power (W) Units
of Total
Power (W)
Processor
1.5
2.300
3.450
1
3.450
DPSRAM
2.4
0.275
0.66
4
2.64
DDR3
1.575
0.260
0.4095
1
0.4095
Microphone
5
0.050
0.25
4
1
FPGA
5
0.300
1.5
1
1.5
Misc
5
0.500
2.5
1
2.5
Total
11.500
Table 3.11- Initial GLASS Design Power Consumption
51
In comparison to the researched current products on the market, GLASS’ power
consumption is right in between THALES Gunshot Detector with a power
consumption of 4 W and Boomerang with consuming 25 W. Also considering
that these calculations are at maximum load, GLASS may very well be the lowest
power consuming gunshot detector if this power consumption is all equally on the
average power consumption, including idle mode. To determine the battery’s
Ampere Hours (Ah) needed, the total power as Real Power (P) is input in the
equation below to first find the Apparent Power (S) in Volt Amperes (VA) given a
Power Factor of 0.9.
S
P
PF
The Apparent Power calculated is 14.5306 VA for GLASS components. Next,
since the battery cells are measured in Ah, the apparent power had to be
converted from VA to A. VA is simply divided by V to result in A. Considering
that the bulk of GLASS’ components utilize up to 5 V during maximum load, 5 V
was the voltage used in the division. Therefore the current to be used in the
battery calculations was 2.9061 A. Considering the runtime necessary of 12
hours, the battery Ah needed is about 34 Ah.
As with any other component, once the theoretical value has been calculated,
finding a suitable component that satisfies the calculated need is almost never
exactly what is needed. There were no 34 Ah specific backup batteries or
uninterruptible power supplies (UPS) on the market and thus a component
component that supersedes GLASS’ specifications was chosen. There were two
UPS that were considered for use in GLASS. The table below shows the
specifications of both UPS and the green highlights which one was chosen.
Backup Batteries
UPS Name
APC BACK-UPS PRO
CyberPower Intelligent
Apparent Power Rating
(VA)
1500
1500
Real Power Rating (W)
865
900
Ampere Hours (Ah)
16
2 x 8.5 = 17
Cost
179.99
149.99
Table 3.12- Battery Backup Possibilities
52
The backup battery chosen, the CyberPower CP1500AVRLCD UPS has two
battery cells rated at 8.5 Ah running at 12 V each. Since our device only ever
utilizes up to 5 V, the conversion from 12 V to 5 V resulted in a total of 40.8 Ah.
Although the theoretical calculations show that GLASS only needed a 34 Ah
battery, the CyberPower battery was chosen with its 40.8 Ah to allow for
manufacturing variation and any other extraneous variables. The table below
demonstrates the calculated values for the CyberPower battery as well as its
relationship to Real Power of the GLASS components.
CyberPower battery
GLASS Real Power (W)
13.0775
Power Factor
0.9
GLASS Apparent Power (VA)
14.5306
GLASS Amps at 5 V (A)
2.9061
CyberPower Cell Ah at 12 V
8.5
CyberPower Cell Number
2
CyberPower Total Ah at 12 V
17
CyberPower Wh at 12 V
204
CyberPower Ah at 5 V
40.8
GLASS Runtime on CyberPower
14.0394
Battery at 5 V (hours)
Table 3.13- Runtime Calculation Table of GLASS on the CyberPower
CP1500AVRLCD Battery Backup
As shown in the table, the CyberPower CP1500AVRLCD UPS actually provides
GLASS with 14 hours of continual running time at theoretical full load in the if the
battery is at full charge and the main power is lost. Considering that GLASS
would essentially be in a low power state until a gunshot actually occurred and
the system started processing the data, this backup battery should provide
almost 24 hours of uninterrupted power if the main power is lost.
53
3.7.7 - Backup Battery Power Source (Solar Panel)
There were two choices when deciding what power source would actually charge
the backup battery. The first consideration was to use a typical 120V AC power
outlet or line to charge the backup battery. The most favorable aspect to using a
typical power outlet to charge the battery is ease of implementation. All this
method takes is to plug the backup battery directly into the outlet and it charges.
GLASS’ initial designs utilized this method because ease of implementation is a
core component to the GLASS design goal. This plug-and-play power source
method also fit the design goal of portability. With the ability to simply unplug the
backup battery from a typical outlet, move the device, and then plug it back in,
the typical power outlet method seemed the winning choice for the better part of
the early design process of GLASS.
However, considering GLASS’ primary purpose: to automatically alarm then
triangulate the position of a gunshot, it was decided that the typical power outlet
method would not be suitable for the backup battery. As mentioned before, the
system’s primary power is sourced through a typical 120V AC power outlet, but in
the case of an emergency and the many variables that could take place in such
an emergency, including the power grid failing, it was decided to charge the
backup battery via photovoltaic solar panel. Unfortunately, the utilization of a
solar panel as the power source for the backup battery reduces the ease of
implementation design goal compared to the use of a typical power outlet. On
the positive side, the addition of the photovoltaic solar panel increases the
portability of GLASS, even if a large solar panel is chosen due to the fact that the
user is no longer required to plug in a second component to a 120 V AC power
outlet. With this change, only the primary power requires an outlet and the
battery backup is a completely independent and self-sufficient subsystem.
Once a photovoltaic solar panel was decided to be the power source of the
battery, it was necessary to calculate the size and wattage output of the panel.
As shown in the above tables, the CyberPower CP1500AVRLCD UPS has a total
capacity rating of 17 Ah at 12 V. The following calculation was made in order to
determine the ampere input needed to charge the battery in a typical eight time
frame of sunlight.
17 Ah
2 125 A
8h
With the necessary amperes needed to charge the backup battery in eight hours
calculated, the research for the correct size photovoltaic solar panel started.
Since solar panel power output is dependent upon not only sunlight exposure,
but crystalline structure, cell array count, and size, there were quite a few options
to choose from for utilization in GLASS. It is important to note that although
GLASS’ component total power consumption may be well under the production
of the solar panels, their specifications are necessary to charge the battery.
Below is a table of the photovoltaic solar panels that were considered to meet the
54
design specifications and the green column represents the component chosen
from GLASS.
Renogy
50 W
Goliath
40 W
Instapark
30 W
Max Power
Voltage (V)
17.6
18.5
18
17.5
Max Power
Current (A)
3.98
2.7
2.22
1.68
Open Circuit
Voltage (V)
22.3
22.7
21.8
21.95
Power (W)
70
50
40
30
Dimensions
(in)
26.7 x 30.7 x 24.8 x 21.3 x 23.3 x 19.5 x 21.5 x 17.25 x
1.4
1.4
1.2
1.125
Cost
$154.99
Panel
Renogy
70 W
$133.99
$79.00
$84.50
Table 3.14- Photovoltaic Solar Panel Options
The decision of which solar panel to choose is a perfect example of how the
overall design goal needs to affect the actual design of the project. Since the
goal of the project is to create a self-sustaining device that is also portable, the
size of photovoltaic solar panel weighed heavily in the decision. As shown in
Table 3.6, although the two Renogy panels have great power, voltage, and
current outputs, their dimensions are too large and the prices too high for
GLASS.
The Instapark panel, while the smallest and most portable,
unfortunately only produces a maximum output current of 1.68 A which would
make the battery charge take over 10 hours from a depleted battery. This is
unacceptable due to the fact that very few places have more than 8 hours of
sunlight in a day and also, the max power current is a maximum theoretical value
which is a best case scenario. Through estimation, the Instapark solar panel
could take upwards of 12 hours charging the battery backup in testing.
Therefore, the Goliath 40 W photovoltaic solar panel was chosen for a variety of
reasons. First, the Goliath has a maximum output current of 2.22 A and
compared to our calculated value of 2.125 A necessary to charge the backup
battery to full from depletion on 8 hours. Although, similar to the Instapark, the
2.22 A value is a theoretical maximum current and in testing would probably be
less, it is much closer to the necessary 2.125 A. Second, the Goliath is actually
the least expensive of all the panels including the Instapark with less
performance.
55
The Renogy 50 W was the top choice until the Goliath was found because it had
a better maximum power current, but the price difference between the Goliath
and Renogy 50 W did not justify the performance difference: a 69.6% increase in
price for a 21.6% increase in maximum power current. Lastly, the Goliath is a
great compromise between the smaller more portable Instapark and the larger
Renogy panels for portability. All of these factors made the Goliath the perfect
choice for utilization within GLASS.
56
Chapter 4
Hardware Design
4.1 Initial Embedded Board Design
The overall custom printed circuit board (PCB) design was modeled after the
Beagleboard BeagleBone Black microcontroller. There are many major
components that are implemented on the custom printed circuit board and those
components play a huge role in the design on the overall embedded board. The
core components of the PCB are: an ARM® Cortex™A9 1GHz processor, a 1 Gb
module of DDR3 DRAM, four modules of 16 x 16 bit DPSRAM, the microUSB DC
power input, data storage via SD card reader, and the peripheral inputs/outputs
including two USB input ports, a Bluetooth input/output device, and the two 46
pin headers. The PCB design was completed within Cadence OrCAD PCB
Editor Software and was fabricated by PCBFabExpress.com. The schematic
below demonstrates the higher level function of the PCB with components
mounted.
Fig. 4.1- High Level PCB Schematic
4.1.1 - BeagleBone Black Microcontroller Model
The stock BeagleBone Black microcontroller was used a model for the custom
PCB design because it generally had all the core components necessary for the
GLASS project. It was important for the GLASS project to have a base circuit
model due to the lack other devices similar to GLASS on the market with open
source research data. The table on the next page displays the specifications of
the BeagleBone Black and what was actually utilized in GLASS.
57
BeagleBone Black/ GLASS
Board Name
BeagleBone Black
GLASS Hardware
Processor
Sitara
Cortex™A8
ARM® Cortex™-A9
Processor Speed
1.0GHz
1.0GHz
Processor Cores
1
2
RAM Size
512 MB DDR3
Audio Input Pins
2x 46 pin headers
1 Gb DDR3 + 4 16 x
16
bit
DPSRAM
Modules
2x 46 pin headers
USB Ports
2
2
Ethernet
1
0
Bluetooth
Yes
Yes
Memory Card
SD
SD
Power Jack
Barrel
MicroUSB
Power Consumption
2.3 W
4.2 W
Operating System
Ubuntu
ARM®,
Custom
Embedded
Linux
Table 4.1- BeagleBone Black vs. GLASS Hardware Specifications
In GLASS, the BeagleBone Black design was the model but not all of the
components are utilized. For example, the BeagleBone Black utilizes an
AM335x ARM® Cortex-A8 processor, which is single core. It was decided to
use a dual core ARM® Cortex™A9 processor in GLASS as multiple cores could
handle our data flow processing with more ease before bottlenecking. Another
change was that in GLASS, the high level graphics processing capabilities were
all grounded given that the project’s overall design first does not need high level
graphics, and second the more capabilities grounded equals lower power. The
memory modules are changed in GLASS as well due to the multiple different
audio inputs from the microphone array and a modified embedded Linux is
utilized in GLASS.
58
4.1.2 - Processor
The ARM Cortex A9 processor is a high performance and low power device
utilized for many applications, most popularly for cell phones. This particular
processor was chosen for the GLASS project for a few reasons. First, the ARM®
Cortex™A9 chosen is a dual core processor and it was decided through
calculation that to successfully process the four incoming gunshot sound
signatures in a reasonable amount of time, a dual core would be necessary.
Second, the ARM® Cortex™A9 has an internal RAM size of 256K x 8 which was
deemed a sufficient amount of memory to process the customized operating
system implemented in GLASS. Third, the Cortex A9 has a processing speed of
1.0 GHz which is needed by GLASS to process the high sampling rate of the
gunshot signatures.
Fourth, the ARM® Cortex™A9 has a plethora of
connectivity and peripheral options including but not limited to: USB, Ethernet,
SATA, PCI, HDMI, and DMA.
Although GLASS does not fully utilize every single connectivity and peripheral
offered by the ARM® Cortex™A9, it was nice to have different options while in
the design process, especially if one connectivity or peripheral that was decided
upon earlier ended up not working out. Lastly, the ARM® Cortex™A9 only has a
Supply Voltage (Vcc/Vdd) of 0.925 V ~ 1.5 V and a maximum current of 2352 mA
making it a low power device at a maximum power consumption of 3.5 W. Low
power was a paramount deciding factor when choosing parts for the GLASS
hardware because it was important for our backup battery to be able to power the
system overnight if necessary. The schematic below is the power connection of
the processor.
Fig. 4.2A- ARM® Cortex™A9 Power Schematic
59
Fig. 4.2B- ARM® Cortex™A9 Power Schematic
Below is the basic architectural block diagram of the ARM® Cortex™A9
processor.
Fig. 4.3- ARM® Cortex A9 Dual Core Consumer Grade System Block Diagram
The next page displays the processor pins (inputs, outputs, grounds, and power)
color-coded for ease of viewing. Reprinted with permission sent to Freescale
Semiconductors.
60
Fig 4.4- ARM® Cortex™A9 Pin Assignment (West Third)
Fig. 4.5- ARM® Cortex™A9 Pin Assignment (Center)
61
Fig. 4.6- ARM® Cortex™A9 Pin Assignment (East)
Fig. 4.7- ARM® Cortex™A9 CPU Signal Schematic I
62
Fig. 4.8- ARM® Cortex™A9 CPU Signal Schematic II
Fig. 4.9- ARM® Cortex™A9 CPU Signal Schematic III
63
Fig. 4.10- ARM® Cortex™A9 CPU Signal Schematic IV
Fig. 4.11- ARM® Cortex™A9 CPU Signal Schematic V
64
Fig. 4.12- ARM® Cortex™A9 CPU Signal Schematic VI
Fig. 4.13- ARM® Cortex™A9 CPU Boot Select
65
4.1.3 - RAM Module Configuration
The RAM utilized in GLASS is unique to the project due to the high sampling rate
and sheer size of data processing power necessary for a computer to sample,
store, and analyze gunshot audio signals from multiple sources. For this reason,
the embedded hardware design utilizes a total of five RAM modules running in
parallel in addition to the onboard RAM in the ARM® Cortex™A9 processor.
The first RAM module is a single chip of 1 gb DDR3 SDRAM specifically utilized
by the ARM® Cortex™A9 processor for calculations, operating system
resources, and GLASS software resources. This module is similar to any other
typical computing system that has a processor and memory. The following three
pages display the ARM® Cortex™A9 processor connections to the 1 Gb DDR3
SDRAM which is split into four quadrants for visualization ease.
The other four RAM modules are necessary for the read-write cycles of the audio
signal processing. These RAM modules are Dual-Port RAM (DPRAM). DPRAM
allows different reads and writes to occur simultaneously rather than one at a
time, therefore preventing a data flow bottleneck. The fact that GLASS utilizes
multiple audio data inputs, the data flow bottleneck would grow exponentially with
each new data input which could possibly crash the whole system.
66
Fig. 4.14- ARM® Cortex™A9 Connection to 1 Gb DDR3 SDRAM West Third
67
Fig. 4.15- ARM® Cortex™A9 Connection to 1 Gb DDR3 SDRAM Center Third
68
Fig. 4.16- ARM® Cortex™A9 Connection to 1 Gb DDR3 SDRAM East Third
69
4.1.4 - DC Power Configuration
The GLASS hardware system is powered by a typical 5 volt DC power supply via
microHDMI. It was decided to use microHDMI because of its popularity and
versatility within the current consumer marketplace and therefore made it a very
inexpensive power solution. The DC power connection for GLASS is also
modeled after the BeagleBone Black power connection as shown in the
schematic below.
Fig. 4.17- DC Power Connection Schematic.
Fig. 4.18- DC Power Over-Voltage Protection Circuit
70
4.1.5 Peripheral Inputs/Outputs
After GLASS processes a gunshot signal, the information: GPS timestamp, GPS
location, and caliber type/size, this data is then output via Bluetooth. The
Bluetooth function allows the system to be wireless and therefore portable,
making it easier to use for the consumer.
The hardware design utilizes a few different input and output connections directly
on the PCB for data. The first of the input ports is two USB jacks used for user
input directly from keyboard and mouse. The second input is actually the stored
data input which an SD card jack. The operating system and extra data files are
stored on an SD card inserted in the SD card jack which allows for the GLASS
system to run while also giving ease of use for code writing because the SD card
can be removed, inserted into another device for coding, and then inserted back
into the GLASS PCB. The SD card socket schematic is displayed below.
Fig. 4.19- SD Card Socket Schematic
HDMI is a function built into the A9 processor and it was decided to utilize it in
the initial GLASS design for the unlikely event that hardware debugging would
need to occur, the user can see the outputs via HDMI. The HDMI circuit is
displayed on the following page.
71
Fig. 4.20- HDMI Connections
For GLASS to accurately triangulate a sound, it is necessary to calculate the
speed of sound which changes with different ambient temperatures. Below is the
thermometer circuit which was designed for the initial GLASS project.
Fig. 4.21- Thermometer Circuit
72
As mentioned before, GLASS utilizes a GPS module to ping for timestamps and
confirm location of the GLASS system.
Fig. 4.22- GPS Module
The following pages display the schematics related to audio capture circuitry as it
is related to how it connects back into the data processing unit. The schematics
demonstrate audio input and audio output as well as some power schematics for
distribution of energy.
73
Fig. 4.23- Audio Schematic I
Fig. 4.24- Audio Schematic II
74
Fig. 4.25- Audio Schematic III
75
4.2 Initial Sound Capture Subsystem
The sound capture subsystem plays a pivotal role in our project given that we are
relying on the sound of firearms to be the mechanism of the recognition of a
firearm discharge and also to be the variable by which we triangulate the location
of the firearm discharge event.
The sound capture subsystem begins with it’s microphones. A carefully arranged
array of 4 microphones will constantly sense or “hear” the environment around it.
The microphones themselves offer very little in the way of signal strength so the
signal of the microphones outputs are amplified through the use of an operational
amplifier based noninverting differential amplifying circuit. The output of the
amplifier is not usable in it’s analog form though however. An analog to digital
converter receives this signal and digitizes it to be in a processable format. The
analog to digital converter is thereby connected to an FPGA. The analog to
digital converter has a serial bitstream output and it is the task of the FPGA to
sort through these bits and store them into a special block of dual port static
random access memory that will hold the data. The FPGA will have an SPI
interface with the analog to digital converter to receive the bit stream and will
have address lines and data lines connected to the blocks of dual port SRAM.
Ultimately the FPGA frees up the processor from performing this task reserving
some processing power for our algorithms.
Overall the sound capture subsystem senses, amplifies, digitizes, and stores the
real world data from our microphones so that the processor may have access to
this data.
4.2.1 – Microphone
There was no shortage of selection in choosing a microphone. The first decision
to be made was whether a digital or analog microphone should be used. Digital
microphones are commonly of the MEMS type. While intuitively we wanted to
pick a digital microphone given the heavy amount of digital signal processing that
will be involved with the project and the ease of connecting such a microphone to
our system, a few realizations were made that excluded the possibility of using a
digital microphone.
On average, most of the digital microphones that were researched showed
inherently average or poor audio characteristics (in relation to our requirements).
Compared to analog microphones, most of the digital microphones with a few
exceptions did not have as good a frequency range, frequency response, nor
sensitivity properties. The only category in which the digital microphones were
evenly matched in with analog microphone devices was in signal to noise ratio.
An example is shown from the specs of the SPM1437HM4H-B:
76
SPM1437HM4H-B
Frequency Range
100 Hz - 10 KHz
Sensitivity
-22 db ±3 dB @ 94 dB
Signal to Noise Ratio
61.5 dB
Impedance
2.2 kΩ
Sample Frequency
1.0 - 3.25 MHz
Pickup Pattern
Omnidirectional
Dimension
4.72x3.76 mm^2
Table 4.2- Microphone Specifications
Structurally, the digital microphones also presented a problem. The sound port of
a digital microphone is often located on the same surface as the terminals of it's
soldering connections. This presents an extra build consideration in that spacers
must be used when mounting the device and also, though the microphone has
an omnidirectional pickup, it is still not ideal and there will be interference due to
the mic being pointed at the circuit board rather than the environment where it
may more easily pick up sound without any fear of distortion due to reflective
surfaces.
Lastly, the digital microphones were shown to be completely unusable by
violating a fundamental tenet of our project design. As discussed earlier on, the
sampling rate of sound is extremely important for the purposes of sound source
triangulation in order to detect the minute difference in event detection times of
the relatively closely spaced microphone array that will be implemented. Our
calculations showed that a sampling rate on the order of a few MHz would be
required to achieve our design goals. A digital microphone is simply a packaged
analog microphone with an analog to digital converter and some method of digital
output such as PDM. Upon evaluating the maximum sampling rate of the various
digital microphones that were looked at, nothing with similar dimensions,
capabilities, packaging, and price of analog microphones was found with more
than a few MHz sample rate, but bearing in mind that PDM formats need to be
oversampled for the proper data output, these microphones are ultimately
insufficient. This was the last straw and the decision to implement an analog
microphone was made.
The choice of an analog microphone still left a vast amount of microphones to
choose from. With choices including piezoelectric, condenser, dynamic, and
MEMS, the microphone chosen had to be aligned with our design considerations.
High on the priorities were a good signal to noise ratio and a wide flat frequency
response.
77
Additionally it was considered that the intended use of the GLASS device might
be in a setting with non-ideal environmental conditions so accordingly, the
microphone should be robust and resilient. And lastly, the microphone had to fit
within the cost constraints of the project. Of the few piezoelectric microphones
that were found, they were immediately ruled out since they were cost prohibitive
being in the 50-100$ range. They also lacked the frequency response and range
needed for our project. Dynamic microphones were also considered as a good
candidate except they lacked the frequency range that was sought. The
consensus came to select a condenser type microphone.
An electret condenser type microphone was chosen as it met our design criteria.
A condenser microphone works (as the name implies) by employing a capacitor
in which one plate is a flexible diaphragm that moves to reproduce the sensed
sound and induce a voltage in the output that reproduces the sound. The electret
microphone operates on this principle as well but employs a different scheme.
Rather than using a capacitor, magnetically polarized particles are distributed in
a dielectric material attached to a metallic diaphragm that is connected to a field
effect transistor that acts as a preamp. The FET is the only part of the
microphone that needs to be powered (in comparison to a standard condenser
microphone in which the coil must have some power source).
Fig. 4.26 - Microphone frequency response
The electret microphone exhibits excellent audio properties that made it our
choice of microphone to employ. The frequency range of our chosen
microphone, the CMA-4544PF-W, extends across the full audible range from 20
Hz – 20,000 Hz. Equally important to the frequency range, the electret condenser
microphone exhibits excellent frequency response. The response of the
microphone is flat from the 20 Hz to approximately 3000 Hz region where there is
then some slight gain in the output lasts until 10000 Hz.
78
The gain then rolls off until 20,000 Hz which is the half power frequency of the
microphone. The microphone seems to be very well designed given these
specifications of the frequency response. The microphone also sports a good
signal to noise ratio of 60 dB.
CMA-4544PF-W
Frequency Range
20 Hz - 20 KHz
Sensitivity
-44 db ±2 dB @ 94 dB
Signal to Noise Ratio
60 dB
Impedance
2.2 kΩ
Voltage
3 - 10 V
Pickup Pattern
Omnidirectional
Dimension
9.7x4.5 mm^2
Table 4.3- Condenser Microphone Specifications
Electret condenser microphones are also known for their durability since they are
enclosed in a metal capsule which further solidified itself as our microphone of
choice. Given the simplistic design of the microphone, we expect it to perform
well in a variety of operating environments making it ideal for our project.
Perhaps as a result of the efficient design the microphone is very affordable at a
price of $0.96 cents per unit.
The actual circuit model is shown below in which a capacitor connected to the
gate of a JFET represents the microphone. A 12 V rail with resistor is tied to the
drain line of the JFET to turn it on and a DC block capacitor is placed at the
output to allow the AC microphone signal to pass through.
Fig. 4.27- JFET Circuit Model
79
4.2.2 - Microphone Array
The design of the microphone array is arranged so that the system may detect
the location of a sound source relative to the GLASS sensor itself. A total of four
microphones are arranged in a pyramidal fashion which allows for our gunshot
source location algorithm to work. Since each microphone lies coplanar to two
other microphones each of edge of equal distance, triangulation can be be
achieved through the manipulation of distance vectors realizing that their
magnitudes differ by a value of the speed of light multiplied by the time
differential between the two microphones .
A fourth microphone is placed in a different plane so that the azimuth (the
vertical angle) relative to the sensor and event source is able to be calculated.
Placing these microphones at orthogonal angles and at equal distance from a
centralized point reduces the calculations to relating four magnitude vectors
together. This arrangement of the microphones allows for location detection in
any area outside of the microphone array.
Fig. 4.28- Microphone Array
80
4.2.3 - Amplifier
The output signal of the electret condenser microphone is very small even with
the built in preamp. As such a gain stage of the sound capture subsystem has
been designed. The motivating force and end goal for the appropriate gain is
governed by the analog to digital converter that is used in the sound capture
subsystem. As with any amplifier circuit, considerations must be made to the
gain, input, and output resistance of the circuit. Given that the AC waveform
coming from the microphone is the only signal that is to be passed to the analog
to digital converter, there is no interest in the DC behavior of this circuit except in
power supply and consumption.
The best approach to this part of the sound capture subsystem was to go with
what is part of the UCF curriculum and employ operational amplifiers for the
amplification stage of the subsystem. The operational amplifier being a
differential amplifier, amplifies the difference in signal input between it's two input
terminals while ideally rejecting the entirety of the common mode signals that
appears at both terminals, practically though some of the common mode signal
still passes and the amount that does is quantified by operation amplifier
manufacturers as the “common mode rejection ratio”.
Initially the UCF lab standard Texas Instruments TL084CD was chosen to
amplify the incoming microphone signal:
TL084CD
Supply Voltage
±15 V
Number of Amplifiers
4
Maximum Differential Input Voltage
± 30 V
Common Mode Rejection Ratio
86 dB
Input Resistance
1000 MΩ
Total Harmonic Distortion
.003 %
Slew Rate
13 V/μs
Unity Gain Bandwidth
3 MHz
Differential Voltage Amplification
200 V/mV
Equivalent Input Noise Voltage
18 nV/√Hz
Table 4.4- Standard Amplifier Specifications
81
The TL084CD was thought to be a good fit for a number of reasons. In the
TL084CD four amplifiers are packaged onto one chip reducing the layout
complexity of the overall design and requiring only one set of voltage supplies.
After this initial decision, it was realized that for the purposes of audio
amplification, more suitable products exist than the TL084 line of operational
amplifiers.
Given the vast number of operational amplifiers available on the market some
were bound to be more suitable than others. Considering the nature of the
project, deciding factors for the selection of an operational amplifier would be one
low noise properties and a high slew rate. Upon researching popular amplifiers
for audio amplification purposes with these requirements, the TL07x series of
operational amplifiers made by Texas Instruments was found to be a suitable fit.
The TL074IDR was considered as it was like the TL084 a four channel chip with
the properties of the TL07x line of operational amplifiers. Upon evaluating this
chip and the layout it would have, it was discovered that this model of operational
amplifier could potentially create noise by having all four channels of the
operational amplifier so close together.
With the numerous resistors required for the implementation of a noninverting
amplifier, the potential for coupling capacitance could occur when two resistors
are close by each other with current flow. With this in consideration, having an
individual operational amplifier for each microphone makes more sense to
reduce any sources of noise in the signal.
TL071CDR
Supply Voltage
±15 V
Number of Amplifiers
1
Maximum Differential Input Voltage
± 30 V
Common Mode Rejection Ratio
100 dB
Input Resistance
1000 MΩ
Total Harmonic Distortion
.003 %
Slew Rate
13 V/μs
Unity Gain Bandwidth
3 MHz
Differential Voltage Amplification
200 V/mV
Equivalent Input Noise Voltage
18 nV/√Hz
Table 4.5- TL071CDR Amplifier Specifications
82
So finally, GLASS will make use of the Texas Instruments TL071CDR
operational amplifier.
The TL071CDR has a very high common mode rejection ratio, low total harmonic
distortion, and is low noise making it ideal for our application.
The basic non-inverting feedback operational amplifier topology has a gain of
However as gain is increased distortion and other effects become apparent so
multiple stages of op-amps are used in the GLASS amplifying circuit. This also
makes impedance matching easier so the desired input and output resistances
can be achieved in stages along with the gain rather than balancing these figures
with one operation amplifier circuit albeit at the expense of cost and space on the
final device. A two stage operational amplifier network was created that is
connected to each microphone.
The electret microphones have an output impedance of 2 kΩ so the input
impedance of the operational amplifier is matched to 2 kΩ for maximum power
transfer. The microphone preamp output will have an output of 10 mV to -10 mV
that must be stepped up to 1.5 V pk. There must therefore be a gain of 150. The
first amplifier stage gives a gain of 15:
The second amplifier achieves a gain of 10:
The overall gain thus becomes 150 giving the desired gain in order to drive the
analog to digital converter.
83
Fig. 4.29 - Amplifier network
Fig. 4.30 - Amplifier gain
84
The application of capacitors was critical in the circuit design in order to block DC
signals from passing through the input, output, and points in between. On the
input line of the first operational amplifier a DC blocking capacitor is placed to
block any DC biasing voltage from the microphone pre-amplifier while allowing
the time varying AC sound signals to pass through. Another capacitor is placed
on the output to block any DC signal on the output of both operational amplifiers.
The operational amplifiers themselves are not perfect and due to non-ideal
effects some DC voltage appears at the output terminal of the operational
amplifiers that needs to be blocked. The voltage at the end of the second
amplifier is that which will pass to the analog to digital converter. Additionally,
capacitors are placed at the DC voltage rails of the operational amplifiers in order
to remove any unwanted AC noise from the line. The capacitor acts a short for
any AC signals that are present in the VCC sources, and are thus mitigated by
going straight to ground rather than into the operational amplifier.
The gain with respect to input frequency was also a design consideration of this
circuit. An AC analysis in NI Multisim was able to show how both output
magnitude and phase are relative to the input frequency of the amplifier. The AC
analysis below shows the circuits frequency response from 10 Hz to 10 KHz. For
the designed circuit the half power frequency is at approximately 20 Hz, the
circuit then achieves a mostly flat gain response by 100 Hz and continues on as
so.
Fig. 4.31 - Amplifier circuit frequency response
85
4.2.4 - Analog to Digital Converter
The analog to digital converter is the bridge between our real world and the one
within any kind of digital system, having applications in audio, video, motor
control, and numerous other categories. An analog to digital converter effectively
samples an input and then holds that value for one period of the sampling
frequency to include it in it's digital output. One important aspect of an analog to
digital converter is the length of the sampling period. This period determines the
sampling rate frequency at which it can sample and convert the analog input.
Another aspect of the analog to digital converter is the input method. Integrated
circuit analog to digital converters often operate in extremely noisy environments.
Whether in an embedded system or a computer, other components of the system
will introduce noise to the signal the analog to digital converter is receiving. The
case being such, a solution is presented by using a differentially driven input.
Just as with the gain process of the operational amplifier mentioned earlier, the
analog to digital converter employs the same technique to mitigate the noise
present in the sampled signal.
Yet another important aspect of the analog to digital converter is it's output mode.
Having a digital bit output, the analog to digital converter has many schemes to
output it's data to the appropriate device. The output formats of the analog to
digital converter can be in either a parallel or serial format.
The parallel output analog to digital converters behave quite similarly to random
access memory in their storage and output of data. They often contain registers
synchronized to a clock line that controls the data output of the device so that the
data may be triggered and latched by the device accessing it. This parallel mode
offers the advantage of quickly accessing the data as every bit of the output word
is accessed at the same time. However it suffers the disadvantage of complexity
and size as many lines must be tied between the analog to digital and controlling
device. The alternative is a serialized output. Rather than reinventing the wheel,
serial outputs often follow a well established serial communication scheme.
These include SPI, QSPI, LVDS, and I2C among others. Each standard having
it's own attributes that may be important to the designers product.
The process of selecting an analog to digital converter proved to be an arduous
task. With so many options the designer can often feel bewildered how to select
the one appropriate for the needs of the project. Initially it was thought the
sampling rate of the analog to digital converter needed to be very high on the
order of tens of MHz. Few products existed at this speed and were quite
expensive. The first device considered was the Integrated Device Technology
ADC1210S065HN/C1:5.
86
ADC1210S065HN/C1:5
Price
$9.39
Maximum Sample Rate
65 mega samples per second
Resolution
12 bits
Number of Converters
1
Maximum Input Voltage
2V
Input Mode(s)
Differentially driven input
Data Interface
SPI or Parallel
Package Area
6x6 mm^2
Number of Pins
40
Maximum Power Consumption
430 mW
Operating Voltage
3V
SNR at Fin = 3 MHz
70 dB
ADC Latency
13.5 clock cycles
Table 4.6- ADC1210 Specifications
The ADC1210S065HN/C1:5 met our requirements but was inefficient in that four
would be required bringing the pin count of the analog to digital block to 160 pins.
This would lead to unnecessary work for moving data and would sacrifice many
lines on the FPGA implemented (as explained further down this document).
The Texas Instruments ADS6422IRGC25 was picked for it’s similar capabilities
to the ADC1210S065HN/C1:5 and having four channels. The ADS6422IRGC25
is a four channel analog to digital to converter that offers an amazingly high
sample rate with good resolution and is a good product in those regards. With
that though, the device is quite large but undoubtedly saves space over the four
separate units that would need to be implemented in the previous device. Each
of the four channels has it’s own separate I/O on the device contributing to 16
pins of the devices 64, nearly 100 pins are eliminated through using this device
rather than the previous one. The ADS6422IRGC25 seemed to be a good choice
for our performance needs at the time. The cost of this chip compared to buying
four of the previous analog to digital converters is somewhat higher but was
decided to be worth the expense.
87
ADS6422IRGC25
Price
$57.72
Maximum Sample Rate
65 mega samples per second
Resolution
12 or 14 bits
Number of Converters
4, separate I/O for every channel
Maximum Input Voltage
3.6 V
Input Mode(s)
Differentially driven input
Data Interface
Serial and parallel modes
Package Area
9.8x9.8 mm^2
Number of Pins
64
Maximum Power Consumption
1.25 W
Operating Voltage
3.3 V
SNR at Fin = 10 MHz
71.4 dB
ADC Latency
12 clock cycles
Table 4.7- ADS6422 Specifications
Upon further research, we found that the initially proposed sampling rate
requirements of GLASS were far above what was actually needed. With renewed
calculations, a 2 MHz analog to digital converter was found to be accepTable
The Maxim MAX11060GUU+ was selected:
88
MAX11060GUU Specifications
Price
$14.40
Maximum Sample Rate
3.07 mega samples per second
Resolution
16 bits
Number of Converters
4 with common output port
Maximum Input Voltage
6 Vpeak
Input Mode(s)
Differentially driven or single ended
Data Interface
Serial (SPI, QSPI, MICROWIRE)
Package Area
6.4x9.7 mm^2
Number of Pins
38
Maximum Power Consumption
1096 uW
Operating Voltage
3.3 V
SNR at Fin = 62.5 Hz
94.5 dB
ADC Latency
405 uS at Fs = 3.07 MHz
Table 4.8- MAX11060 Specifications
This analog to digital converter is a very good fit into our design. With the
decrease in sampling frequency, more options became available and the Maxim
was chosen for it's specifications as listed above. Compared to the TI chip, the
Maxim is a good deal less expensive being $43.32 cheaper than the TI chip. It
also has the advantage of being smaller and having less pins to be dealt with.
However it does lose some of the versatility of the TI analog to digital converter in
that it only offers a serial output stream and does not feature a parallel output
mode. However it does support many standards of serial output. The output is
unique in that all channels leave one output port on the device in a serial stream
rather than each channel having it's own output.
This has the disadvantage of increasing the access time of the data but does
reduce the complexity of the system and the number of lines to the device. Also
of importance is the high SNR of the device.
With an expected input frequency on the order of hundreds of Hz, the Maxim
chips seems to have excellent SNR properties with an SNR of 94.5 dB at 62.5
Hz.
89
A scheme of data retrieval now had to be developed for the analog to digital
converters output. The output of the Maxim analog to digital converter is a serial
bit stream. Sorting of this data to be put into system RAM is essential so that
data can be accessed by the processor for computations and calculations. Given
the nature of the task, it was a decided that implementing an FPGA to sort
through the analog to digital output would be most appropriate.
4.2.5 - FPGA
The general procedure of the FPGA is to capture the output bits of the analog to
digital converter and store them into the dual port SRAM of the device noted
later. The FPGA will thus has the task of communicating with the Maxim analog
to digital converter in order to latch the bits of the output stream and furthermore
is tasked with addressing and writing the output words of the Maxim into
DPSRAM. The first task was to identify a proper FPGA that would allow this
operation.
Sticking to what is familiar, it was first decided that a Xilinx FPGA should be used
as the UCF curriculum employed the use of Xilinx products and design suite in
the Digital Systems course. Particularly, a Xilinx Spartan 3 series FPGA was
chosen. Initially the same FPGA used in the digital systems laboratory was
chosen, the Spartan 3E XC3S100E-4VQG100C.
XC3S100E-4VQG100C
Cost
$10.51
Number of Gates
100k
Number of user I/O Lines
66
Block RAM (18 Kbit units)
72K
Supply Voltage
1.2 V
Number of Pins
100
Area
16x16 mm^2
Table 4.10- XC3S100E Specifications
Initially this FPGA was thought to be sufficient, however upon recognizing the
implementation of the dual port static random access memory (discussed below),
it was realized many more I/O lines would be needed than the 66 that the 3100E
offered and that this device was optimized for gate count and not I/O operations
so another device was to be chosen.
90
Given the input output nature of the operation to be performed, the Xilinx Spartan
3A was selected. The Spartan 3A series is optimized for input/output operations
and sacrifices the gate density of the chip for an increased pin count and an
overall lower price. The XC3S200A-4FTG256C was the chip decided upon:
XC3S200A-4FTG256C
Cost
$14.90
Number of Gates
200k
Number of user I/O Lines
195
Block RAM (18 Kbit units)
288K
Supply Voltage
1.2 V
Number of Pins
256
Area
17x17 mm^2
Table 4.11- XC3S200A Specifications
For an additional $4.39, the XC3S200A-4FTG256C offers nearly three times as
many I/O, twice as many logic gates, and four times the block RAM. The number
of I/O lines is sufficient for the addressing and data connections required
between the FPGA, analog to digital converter, and the DPSRAM.
The XC3S200A-4FTG256C thus gives a good margin of safety with regards to
the number of I/O lines available for use. The FPGA will be connected to the
analog to digital converter utilizing an SPI connection for ease of implementation
and design simplicity.
91
4.2.6 - DPSRAM Audio Buffer
Glass implements its audio buffer with four CY7C026A 16k x 16 bit Dual Port
Static Random Access Memory(DPSRAM) cells.16k x 16 bit is necessary to
provide adequate room to buffer incoming information. Asynchronous DPSRAM
was chosen to take advantage of the microcontroller’s asynchronous bust mode
which allow the External Input Module (EIM) to take in a variable length block
from memory at a time. The CY7C026A was chosen because it was determined
a feasible solution having a fast access time, and less costly price, when
compared to other DPSRAM of equal size. while also providing adequately fast
speed within the same price range as cells with a 55 ns speed some of which
were not RoHS compliant.
DPSRAM
CY7C026A
IDT70V261L25PFI
IDT7026S/L
Min.
Operating 4.50
Voltage (V)
3
4.50
Max.
Operating 5.50
Voltage (V)
3.60
5.50
Min. Operating Temp. 0
(°C)
-40
0
Max. Operating Temp. 70
(°C)
85
70
Speed (ns)
20
25
55
Density (Kb)
256
256
256
Organization (X x Y)
16Kb x 16
16Kb x 16
16Kb x 16
Price
36.8
44.13
34.74
Table 4.12- DPSRAM Product Determination
The functionality of Dual Port Static Random Access Memory(DPSRAM) allows
one data line to access the data independently of the other port. This is
necessary in order to ensure that the processor is kept free from the tedious data
buffering that could potentially result in the processor being unable to fork the
multiple thread to triangulate to the sound source and perform the data
correlation. 16k x 16 bit was chosen to allow the audio buffer to store enough
information without overwriting a signal before the microcontroller is able to
retrieve it. With ours sampling rate at 1M sample per second and a rifle’s sound
signature lasting for less than 5m seconds, a minimum of 5K words are
necessary to retrieve the information. Also the length per recording must also be
92
kept low to accommodate the speed of automatic weapons fire. A minigun, for
instance fires a round every 10m seconds. If GLASS continues to grab
information for longer than that period. Multiple sound event will be treated as
part of the same source and will cause problems when attempting to correlate
with the sampled audio signals. GLASS processes information through a time
frame of 5 ms. With a16K word buffer depth and 1 M samples per second the
buffer will be filled in 16ms.
The CY7C026A has an access time of 20ns meaning the absolute maximum
access rate of 50MHz. The microcontroller must access this data four times;
twice because there are 64 bits of data and 32 data lines and twice again to
access the semaphores. This means the microcontroller may access the data at
a rate no more than 12.5MHz, which is sufficient to ensure the buffer is built at a
slower rate than the microcontroller reads it. At 12.5 MHz the buffer will be read
2
in no less than .400ms + 5k writes to DDRAM at 10663M T/s=.4047ms. The
microcontroller is alerted after 5ms though it may receive the when pinging the
FPGA after 4.6ms. Within this time range the buffer has sufficient time to fill
without the microcontroller passing the write point. During this period the
processor has 400k cycles will have passed per core.
The address lines for the input side are controlled by the Field Programmable
Gate Array (FPGA) and are equivalent for each buffer allowing simultaneous
writes by the FPGA and reads by the EIM. On every sample received by the
Analog to Digital Converter (ADC) the FPGA buffers the input serialized
bitstream until it has 16 bits per microphone. Then the FPGA writes to each cell
of DPSRAM its corresponding 16 bits. The address register is then incremented
by one. When a value with sufficient amplitude is sampled this address is stored
in a register to be given to the microcontroller. Periodically the microcontroller
pings the FPGA to determine if sufficient data has been buffered. Once this
condition is met, the FPGA will output a valid address in the buffer’s memory.
GLASS’ processor Interfaces with this buffer via the EIM. On a read the address
bus makes it possible to address 128M words of external memory from four
separate devices sources. GLASS currently uses only 32k words of data which
results in 64k words for the audio buffer which is used in order to read the
semaphores of each memory address the DPSRAM device must utilize the least
significant bit of the address bus as the identifier for the semaphore. Therefore it
may be expanded to include additional microphones or other input devices for
the same device. These other 127.936 M words are where, in memory, reside
the other signals which the correlation algorithm uses for the recognition of a
gunshot.
Interfacing with the EIM can be seen in Figure 1 and Figure 2 on the following
pages. Although additional memory can be allocated to the same device as the
audio buffers, it is better to separate them by device type. The memory holding
the recorded signals is connected in the same manner as the DPSRAM featured
93
below, however one exception exists for the Chip Enable(EC) flag. DPSRAM has
the characteristic that in order to read or write to the semaphore flags for each
address the CE flag must be high(chip is off) and the semaphore flag must be
low. For this reason the DPSRAM must include a two to one multiplexer to switch
between the the CE flag and the least significant bit of the address line. Without
this mux when the device is turned off it will still output the semaphore flag for
whatever is on the address line given that the address is odd. For regular SRAM
this kind of behavior result in the chip being disabled when performing a read to
odd memory locations.
94
4.32 Figure 1 The Left 32K block of DPSRAM as it connects to the processor
95
Fig. 4.33- the right 32K block of DPSRAM as it connects to the processor
96
4.3 Initial Power Subsystem
The power connection for the GLASS hardware is a typical 5 V microUSB.
These 5V are sufficient to power all other components on the board as well. The
microUSB sources its power from two different places. First, the primary power:
a typical 120 V AC power outlet and then a secondary power source: a battery
backup which is charge by way of photovoltaic solar panel. Smaller components
such as the decoders, multiplexers, etc also draw from both sources voltage
levels must must be reduced for each specific device.
4.3.1 - Primary Power
Similar to any typical electronic device powered in the United States, the GLASS
hardware’s primary power is sourced from a 120 V AC power outlet. The
hardware only takes in a 5 V input from the microUSB which has a built in AC to
DC power converter as well as resistivity to lower the voltage from 120 V to 5V.
There were a few options to choose from when it came to the main power input.
An original consideration was to actually embed a power cord right into the PCB
leading directly to a AC to DC converter and subsequently to the AC outlet. This
idea was scrapped because of the GLASS’ initial design expectation to be
extremely portable and easy to install. Once it was decided to not to
permanently attach the power cord to the PCB, but to implement a removable
cord, there were two major options that presented themselves: the typical 5V DC
Barrel jack and the microUSB inputs. After much consideration and careful
research, it was decided that in the implementation of GLASS, the MicroUSB B
jack would be more suitable than the Barrel jack. It was found the MicroUSB B
inputs have been much more standardized within the industry compared to Barrel
jacks which come in many sizes, voltage ratings, current ratings, and so forth.
For convenience and end cost to the user, microUSB B was deemed a better
choice.
4.3.2 - Secondary Power
Since GLASS in general is an emergency response device, it was decided in the
very early stages of planning that a backup power source would be necessary in
the design.
The secondary power is implemented when the primary power source is lost or
disconnected for whatever reason. Once primary power is lost, the GLASS
hardware draws its power from a 1500 VA, 900W computer backup battery. This
battery is charged by way of a 30W mono-crystalline photovoltaic (PV) solar
panel.
97
Fig. 4.34 - Primary and Secondary Power through MicroUSB Schematic
98
4.3.3 - Power Switching
The system switches automatically from primary to secondary power using
Intersil’s ICL7673 Automatic Battery Back-Up Switch. The switch was designed
to ensure immediate power supply switching making sure that the data within the
volatile RAM in the GLASS system is not corrupted or lost. The design works by
logic gates driving external PNP CMOS transistors to switch to whichever power
supply has a greater voltage. The below figure demonstrates the basic design of
the power switching circuit.
Fig. 4.35- Automatic Battery Back-Up Switch Typical Diagram. Reprinted with
permission sent to Intersil.
99
4.4 Current Proof-of-Concept Hardware Design
The current GLASS design demonstrates the team’s ability to design and
populate hardware, as well as program software in order to complete the
necessary task. This simplified design utilizes only two of the three modules in
the initial design and distributes the workload differently.
GLASS monitors for a certain signal peak and calculates the difference in peaks
between the microphones. This calculation is then sent via wi-fi to an Arduino
Uno development board which is connected to a PC via USB.
The information sent from the Primary Module is forwarded to the PC by the
development board where the information is processed and displayed. The
development board is just a bridge for the information between the Primary
Module and the PC. The PC is programmed with the triangulation and gunshot
recognition software. This is proof-of-concept because pushing the computation
to a PC confirms the ability to process the data the way initially intended if
resources were adequate.
Feature
Hardware Specifications
Processor
ATmega328P 8-bit AVR RISC-based microcontroller,
32KB ISP flash memory, 1024B EEPROM, 20 MHz
Operating Frequency.
Microphones
SPM1437HM4H-B 100 Hz-10 KHz Frequency Range, 22 db ±3 dB @ 94 dB Sensitivity, 61.5 dB Signal to
Noise Ratio, 1.0-3.25 MHz Sample Frequency
Battery Backup
CyberPower LCD 340 W, 600 VA, 7Ah Uninterrupted
Power Supply
ISM Transceiver
nRF24L01+ 2.4 GHz, 250 kbps-2Mbps Data Rate, 4
pin SPI configuration, 1.9-3.6 V Operating Voltages
GLASS to PC Data Arduino Uno development kit with ATmega328P.
Bridge
Table 4.13- Current GLASS Hardware Specifications
The current hardware design for GLASS condenses the many different complex
parts in the initial design into a more manageable design. The most notable
simplification is the custom board design being cut from a four layer, 25
schematic design down to a simple two layer, one schematic design. The audio
and power blocks were also simplified to better fit the scope of the project
timeline and resources.
100
4.4.1 – Audio Capture
The current audio sampling section still consists of the four microphones
equidistant and on separate planes and the same peak detection equation is
used to determine signal peaks. The main change to the audio block is that the
custom board design has moved from the data processing block to the audio
block. The audio samples are read directly by the custom GLASS PCB which
now utilizes an ATmega328P processor. The ATtmega328P has built in ADCs
simplifying the audio design by eliminating a separate ADC module and FPGA.
After determining peak, the GLASS PCB then forwards the data via wireless
using the nRF24L01+ wireless module.
4.4.2 – Data Processing
The current GLASS PCB is shown below. The decision to simplify the hardware
design was necessary to the completion of the project on time. Unfortunately,
the simplification made GLASS lose a significant amount of processing power.
Due to the loss of the more complex and powerful Cortex A9 ARM processor, the
current GLASS board simply functions to capture sound, pre-process the signals,
and forward the data to an outside computer device. Nevertheless, the
simplification still demonstrates the ability to design and produce a functioning
system.
Once the GLASS PCB forwards the audio data over wireless, the data is
received by an Arduino Uno. This Arduino device acts a bridge between a
computer running GLASS software and the wireless module receiving the data
from the GLASS PCB. For the presentation of GLASS, a laptop with Windows 7
will be running the GLASS software that determines the gunshot sounds and
locations.
Fig. 4.36- Current GLASS PCB Gerber Board Schematic
101
Fig. 4.37- Current GLASS PCB Schematic
4.4.3 – Power
For ease of presentation and system setup, the solar panel has been removed
from the power block and GLASS will run entirely off of the UPS battery at full
charge. The previous time of use calculations are actually improved with the
current design for the new GLASS PCB does much less processing and
therefore consumes less power. Considering that the power consumption of the
new system is 15% of the original design, the UPS was downgraded to 340 W
still enabling GLASS to run longer than it did with the larger battery in the original
design as demonstrated below.
Voltage
(V)
Current
(mA)
#
Power (W) Units
5
16
0.081
1
0.081
Arduino Uno 5
50
0.250
1
2.50
DDR3
1.25
200
0.250
4
1
Wireless
Modules
3.3
11.3
0.037
2
0.075
ATmega328
Total
of Total
Power (W)
1.406
Table 3.14- Current GLASS Design Power Consumption
102
Chapter 5
Software Design
In order to properly compose the software for GLASS the embedded system
must be understood at a high level. To effectively utilize the microcontroller the
task of retrieving data from the buffers, their processing and storage into memory
is multithreaded. The location and weapon type determination processes run,
and lastly the data is published to the user.
5.1 Embedded System Design
Glass’ System, see Figure 5.1, consists of four major components, the Core
Processing Board, Audio Interface and User Interface and a power system,
which was discussed in Chapter 4. The Core Processing Board Serves as an
intermediary between the Audio Interface and User Interface; processing the
data collected through the Audio Interface and publishing pertinent information to
the User Interface. The final result being an alert showing the location where the
gunshot originated from on a map.
In order to effectively access the data in the audio buffers, the software will do
some rudimentary processing to the data as it is read from the buffer. As each
frame of time is read in by the processor, a counter is kept to determine where
the maximum value for each microphone in the array. Also since the discrete
time wavelet transform is defined with a summation, the wavelet can be
constructed as the data is read.
Another method to decrease memory access times is to multithread the the data
acquisition and processing task to half of the microphone arrays. Although the
microcontroller can only access the buffers two arrays at a time, One thread can
wait while the other grabs the sample for the current time frame. While the first
thread is processing the data to perform the wavelet transform and finding the
maximum values and time frame, the second frame is free to access the sample
for the other two microphones.
103
5.1- Glass Embedded System High Level
All signal sampling and conversion takes place in the Audio Interface. Four
microphones connected to an analog to digital converter, whose output is then
fed to block Dual Port Static Memory(DPSRAM), via Field Programmable Gate
Array(FPGA), connected to processor in the Core Processing Board. the FPGA
serves as an intermediary between the memory and Analog to digital converter in
order to convert the serialized binary stream into parallel data that can then be
fed to an incrementing location in memory in the form of DPSRAM. Doing so
gives Glass the opportunity to use the built in External Inp(SDMA) controller so
the processor may Access the captured data while also bypassing the processor
104
to freeing it to take care of other tasks. An interrupt is sent to the Core
Processing Board when a signal of sufficient amplitude is received at which point
execution of the Gunshot Recognition and the Triangulation algorithms
commences.
In the Core Processing Board the microcontroller runs Linux in low power mode,
monitoring power consumption. Upon receipt of the interrupt from the Audio
Interface, the operation system forks to create threads for both the Gunshot
Recognition and the Triangulation algorithms. After processing the data, the
system communicates with an Android device, sending it pertinent information,
including date, time, and a vector from the node that received the Gunshot event.
Its processor is an dual core ARM cortex A9 micro controller with a clock speed
of 1GHz.
Since the time between the echo and main signal increases, Glass’s processor
will only need to retrieve the first 5 milliseconds of data amounting to 5K samples
of 16 bits (two zeroes included in msb)across four microphones each, resulting in
320 kbits of data or 80 kBytes. considering our processor has a 32 bit data bus
that leaves 20k words to be transferred for each gunshot event. This Is achieved
by pairing two of the microphones to each address location in memory. The data
line is then shared by half of the 32 bits for each buffer meaning that for each
sample data must be read by the processor twice.
The buffers are 16k deep this means that once an address with sufficient
amplitude is sampled Glass has 15 milliseconds until the first data point will be
rewritten. As glass has a Dual Core 1GHz processor The best we can hope for is
that 32M cycles will have happened at this point.
The User Interface (UI), hosted on an Android device communicates with the
Core Processing Board via Bluetooth. Bluetooth was chosen as the transmission
medium because GLASS is intended to be installed in or around buildings where
GLASS would have access to relaying devices within range so that it must not
send long range transmissions to offsite locations. Instead the UI is intended to
filter out duplicate signals, when other nodes are in range, and forward
information through reliable connections.
Upon receipt of a message, the UI displays an alert allowing the user to check
the interface for the received information. On entering the application, the user
sees a map of the area near the node that transmitted the message and the
location in which the event occurred relative to the node. The event is given a
date and time and which weapon corresponded to the event. Communication
with the UI is unidirectional and changes to the device must be made through the
system directly.
105
5.2 Linux
Linux was chosen as the operating system for Glass due to Its portability and
large development and support base. Access to the source code for the
operating system allows Linux to be modified to our custom hardware, and adapt
existing drivers to meet Glass’ needs. Specifically the Ubuntu as it is already
configured for the Beaglebone Black. Ubuntu also has a large support base and
is a popular distribution of Linux. Unlike Android, Ubuntu can easily have
components modified or outright removed. Android may be optimizing many
common mobile related applications, GLASS does not utilize a majority of these,
rendering embedded Linux a more viable option.
On system start GLASS boot from processor ROM. It is then loads the operating
system and begins operation. Upon receiving the interrupt Linux will drop into
Kernel mode and execute a function to place the relevant data in memory to be
processed later. Then the operating system will switch back to user mode and
fork two threads; one for the gunshot recognition and another for the location
detection.
Lastly the process for sending the information to the user interface runs as a
thread sleeping until awoken by the termination of both threads. This thread
establishes communication with an Android enabled device. Then packets are
sent via Bluetooth containing the time, location and calculated armament type.
The User Interface on the Android device then displays the pertinent information.
5.3 Gunshot Recognition Algorithm
The first concern in the gunshot algorithm is to normalize the input signal. With
varying distances and angles from the microphone array, the likelihood that the
sample levels match any recorded data is unlikely. Using the wavelet Transform
on the data gives Glass the ability to find the low frequency components
associated with gunfire. Also the wavelet transform shows the amount of time
each frequency component occupies on the signal which helps to determine
when a gunshot is between two different weapon types which may be considered
closer.
Special care must be taken to remove the echoes which have a larger effect on
the signal. As the distance from the microphone array increases the decibel level
of the echoes grows relative to the magnitude obtained from the original signal.
This is due to the non linear growth of the of the distance traveled by the sound
C->A and echoes,C->B->A see Figure1. Also the distance in arrival between the
two signals decreases because the length of side c becomes more and more
trivial, making the normalized echo amplitude closer to the amplitude of the direct
signal.
106
5.2- Sound as it travels from source to microphone along vector b and as the
echo travels along the paths of vector a then c
Recognizing the weapon type is accomplished by finding the correlation between
the signal and a stored sample. The sample which results in the highest
correlation should then be assumed to be the weapon type. This tells us how
alike the two signals are. Since the Influence of echoes becomes more and more
problematic at the max decibel level decides which pre-recorded to do the
correlation against. By sorting the recordings be amplitude, Glass can reduce the
number of comparisons to make as a revolver at 1m may have the same
amplitude as a rifle at much farther, however the correlation between the two will
result in a lower correlation value.
However that is insufficient as even non weapon based sounds would incur a
correlation value. For that reason the spectral components of the signal must be
examined to determine that the signal did indeed come from some kind of arms
fire. By applying the wavelet transform on the signal the spectral components can
be matched to the requirements that characterize a gunshot. these include a
super sonic signature, an initial blast from the powder, and a shock wave as the
bullet travels through the air.
5.4 Location Algorithm
Determination of the source of the signal is fairly straight forward, though it
requires somethings to be taken into consideration. GLASS determines the
source of a sound event by relating the change in time between the sound’s
arrival between two microphones in the array and First is the effects of
temperature on the speed at which the sound can propagate through the air. For
that purpose there is a digital thermometer feeding the temperature to the
system. The speed for sound for a given temperature is given using the practical
formula for dry air thusly:
C(T)=331 3√1
T
273 15
Where T is in Celsius, and the result is in
m
s
107
In Order to determine the position of the gunfire the time difference between two
nodes must be determined. this is done by first finding the point in the signal
where the signal reaches its maximum for each microphone the time delay can
then be calculated by the number of samples difference the maximum occurs
from each other. Each microphone is stationary at equal distance from the point
of origin, and vectors from the microphones to the origin are orthogonal. Distance
vectors must then be related together in that the magnitude of one distance
vector is equal to the magnitude of the other and the algebraic sum of the speed
of sound multiplied by the difference in time between where the maximum value
occurs in each signal. Distance vectors may then be drawn from the sound
source to each microphone in the array, see Figure 1.
5.3- Distance vectors drawn from the source to all microphones in GLASS’s array
The magnitude of each distance vector can from the sound source to the
microphone can be characterized by:
√ x xi
2
y yi
2
z zi
2
Where the microphone ith is at location (xi,yi,zi). Then any two microphones can
be related as:
108
√ x-xi
2
y-yi
z-zi 2 =√ x-xii
2
2
y-yii
2
z-zii
2
C T Δt
where C(T) is the speed of sound at the current temperature, and Δt is the
difference in time between the sound arriving at microphone i and microphone
ii.From the two microphones that are on the same x-axis, vectors B and D, they
can be related as:
√ x
xd
2
z =√ x-xd
y
2
y
z
C T Δtbd
Since xd yc za -xb D. Once the maximum values between two nodes
has been calculated and the ambient temperature has been used to determine
the speed of sound, C(T)Δti can be thought of as a constant Ki thus:
x
D
2
y
z = x-D
4D x -K2bd =2Kbd √ x
D
2
y
z
2
y
z
4D x
8DK2bd
16D2 x2 8DK2bd x
2
2Kbd √ x
K4bd
x
2
D
4K2bd x
y
D
2
y
8DK2bd x
4K2bd D2
16D2 x2 4K2bd x2 16DK2bd x 4K2bd D2 K4bd
4D2
-2
z
KK2bd
4K2bd x2
Eq.1 4D2 Kbd -1 x2 -4D x
K2bd
z
x2 x2
2
Kbd
1
-D2 - 4 K2bd
4D x D2
y
1 2
K
4 bd
4K2bd y
4K2bd y
y
4K2bd z
4K2bd z
z
z
The following relations can be drawn from the other microphones. By relating
vectors A and D the magnitude can be reduce to:
√ x-D
x-D
2
2
z =√x2
y
y
-2Dx=-2Dz
y
2
z-D
Kda
=x2
y
z-D
2
Kda √x2
2Kda √x2
y
z-D
2
K2da
z
2
2Dz-2Dx-K2da =2Kda √x2
4D2 z2 -8D2 xz-4DK2da z
y
z2 -2Dz
y
z-D
2
K2da
D2
4DK2da x-4D2 x2 -K4da =4K2da x2
4K2da y
z2 -8K2da Dz
4K2da D2
109
2
2
2
4D z2 -8D xz
2
2
4DKda z=4Kda x2
-2
-2
Eq.2:D2 Kda z2 -2D2 Kda xz
2
Dz=x2
z2 -Dx
y
4
z2 -4DKda x
4Kda y
1
4
K2da
Kda
2 2
4D Kda
D2
Vectors C and D can be related as follows:
√ x-D
x-D
2
2
z2 =√x2
y
z2 =x2
y
-2Dx=-2Dy
y-D
y-D
2
2Kdc √x2
2
z2 -8K2dc Dy
4D2 y2 -8D2 xy
Kdc √x2
y
y-D
z2
K2dc
2
K2dc
2
D2
y2 -2Dy
z2
y-D
4DK2dc x-4D2 x2 -K4dc =4K2dc x2
4K2dc y
4K2dc D2
4DK2dc y=4K2dc x2
2
-2
Kdc
z2
2Dy-2Dx-K2dc =2Kdc √x2
4D2 y2 -8D2 xy-4DK2dc z
z2
-2
Eq.3: Kdc D y2 -2Kdc D2 xy
4K2dc y
Dy=x2
z2 -4DK2dc x
z2 -Dx
y
1
4
K2dc
K4dc
2
4D2 Kdc
4D2
Vectors C and A can be related as follows:
√x2
y-D
x2
y-D
2
2
z =√ x2
z =x2
-2Dy=-2Dz
y
y
z-D
2Kca √x2
4D2 z2 -8D2 yz-4DK2ca z
4D2 z2 -8D2 yz
2
2
y
2Dz-2Dy-K2ca =2Kca √x2
z2 -8K2ca Dz
2
z-D
Kca
Kca √x2
z-D
y
y
z-D
K2ca
2
K2ca
2
z2 -2Dz
D2
4DK2ca y-4D2 x2 -K4ca =4K2ca x2
4K2ca y
4K2ca D2
4DK2ca z=4K2ca x2
2
-2
2
Eq.4:K-2
ca D z -2Kca D yz
4K2ca y
Dz=x2
y
z2 -4DK2ca y
1
z2 -Dy
K
4
2
ca
K4ca
2
4D2 Kca
D2
By substituting equation 1 into equation 2 the following relation can be made:
-2
-2
D2 Kda z2 -2D2 Kda xz
Dz=x2
-2
1
4D2 Kbd -1 x2 -4Dx-D2 - 4 K2bd -Dx
1
4
K2da
D2
110
-2
-2
Eq.5:D2 Kda z2
-2
1
1
4
4
D-2D2 Kda x z=4D2 Kbd x2 -5Dx- K2bd
K2da
By substituting equation 1 into equation 3 the following relation can be made:
-2
-2
D2 Kdc y2 -2D2 Kdc xy
-2
Eq.6:D2 Kdc y2
-2
1
4D2 Kbd -1 x2 -4Dx-D2 - 4 K2bd -Dx
Dy=x2
-2
-2
1
D-2D2 Kdc x y=4D2 Kbd x2 -5Dx- 4 K2bd
1
4
1
4
K2dc
D2
K2dc
Subtracting equation 5 from equation 6:
-2
-2
D2 Kdc y2
1
4
-2
K2dc -4D2 Kbd x2
-2
-2
-2
-2
1
D-2D2 Kdc x y-D2 Kda z2 - D-2D2 Kda x z=4D2 Kbd x2 -5Dx- 4 K2bd
Eq.7:D2 Kdc y2
1
K2 - K2
4 bd 4 da
-2
-2
D-2D2 Kdc x y-D2 Kda z2 -
5Dx
1
-2
1
1
4
4
D-2D2 Kda x z= K2dc - K2da
With these equations we may construct the paraboloids necessary to solve for a
given sound source. Then the point is calculated and stored to be sent to the
user interface by a separate thread.
111
Chapter 6
Project Prototype Construction
6.1 Hardware
6.1.1 – Initial Design
The initial hardware prototype will be built on a circuit breadboard in multiple
sections. The purpose of prototyping in sections is to eliminate time wasting
troubleshooting on a much larger circuit if the entire design was prototyped at
once. This way, if there is a problem with a single component in a subsystem,
the problem can be resolved quicker within the subsystem prototype.
The first section to be built as a prototype is the power system. The PCB
integrated power system will be prototyped with the primary to secondary power
switch connected to the primary power source, a 120 V AC outlet.
Measurements will be taken entering and exiting the power source switch to
ensure that the proper amount of power is traveling completely through the
switch. Also, the power output of the PCB power circuit will be tested to ensure
the voltage and current are safe and consistent for the rest of the electronics in
GLASS. Since the secondary power source is a photovoltaic solar panel feeding
a backup battery which were both purchased whole, no prototype will be made
and they will be tested in the testing stage. The primary power system prototype
is to ensure that the system will actually receive at least the main power source.
Next, the audio capture subsystem section: microphones, operational amplifiers,
and analog to digital converters will be all combined in a circuit that will feed a
data stream to the FPGA. The output of each stage will be monitored to ensure
the data is consistently being read and initially processed through the FPGA.
This section of the prototype may prove to be problematic due to the multiple
microphones all having to be read by their respective analog to digital converters
and then fed one at a time to the FPGA.
After the audio section is prototyped, the four DPSRAM modules will be added
to the audio section connecting the four bit streams to their respective DPSRAM
module. The output of each DPSRAM will be monitored to verify that each
bitstream is being buffered into the memory.
Seperately, the ARM processor, DDR3 DRAM module, and peripheral ports will
all be prototyped on the breadboard to confirm that the connections are valid.
Once this is done, the audio section with the additional DPSRAM and primary
power circuit will be connected to the processor and the entire circuit will be
tested to see in the processor actually receives the audio data.
112
6.1.2 – Current Design
The current design was prototyped in sections similar to the initial design. The
microphone circuits were built on breadboards and the inputs/outputs were
checked for accuracy. The PCB was more difficult to prototype with the many
more connections needed for the ATmega328 processor. It was done by placing
the Atmega328 on a breadboard and programming the processor using a circuit
topology demonstrated by the Arduino website.
6.2 Software
The algorithms for the location and gunshot recognition will first be developed in
matlab using a prerecorded sound. Once the mathematical tasks are complete,
they will be integrated into the GLASS system as C++ code. The operating
system will be configured on a separate computer and placed into the board on
an SD card. The system will then boot from this SD card. Once the algorithms
have been prototyped and found working, they will be integrated to work on a
linux run virtual machine. The primary tasks for this stage of design is to tune the
multithreading and process creation aspects of the system to ensure that the
microcontroller can performs the parallel data processing required on the actual
hardware. Also the Bluetooth and GPS will have their drivers developed in this
mode.
After the Virtual Machine mode of our development is completed, the software
will be ported to a Beaglebone Black for debugging under an ARM system.
Although GLASS does not run on a Beaglebone Black, the similarities in their
microcontrollers and fact that GLASS’ general system hardware design is
modeled after the Beaglebone, makes it a logical choice to prototype all software
on. Since the Beaglebone shares a similar processor to ours, and our basic PCB
layout is modeled after it, This will be the portion where the device specific bugs
should occur. The major concern at this stage is that the code written in an Intel
architecture works after it is placed in its ARM system. Naturally this stage
requires the user interface to be virtually completed and only testing of the
communication between the system and the Android device should remain. This
phase also Includes the early tests of communicating with the Android device as
the GPS will be connected for the first time.
Lastly the software is loaded onto the GLASS architecture. at this point there
should be only minor bug due to the difference between the microcontrollers.
Afterwards comes the tests and trials of the system on the “field.” Field testing
includes reproducing sound signals from a speaker and determine if the source
location is correct. Testing in this phase will be developed in more depth in the
next chapter.
113
Chapter 7
Fabrication and Testing
7.1 Hardware
7.1.1 Initial Design
After the prototype is successful and the design schematics are finalized, the
custom PCB design will be sent to PCBFabExpress.com for fabrication.
PCBFabExpress.com will take the finished PCB schematics and combine the
multi-layered design and produce a physical board. Once the PCB is returned to
the GLASS group, all of the PCB destined parts from prototyping will be mounted
on the PCB with the use of a plate soldering tool. The processor, five modules of
RAM, USB ports, bluetooth, power circuit, SD card input, microUSB power input,
and input and output pins will all be embedded onto the PCB at this point.
Once the PCB is fabricated and all the necessary components are mounted onto
the board, the primary power system will will be connected to the board via
microUSB port. The board will be tested for general functionality by powering it
on and running at least the embedded linux operating system through the SD
card. After the primary power system is confirmed operating successful, the
secondary power system will be tested. First, the photovoltaic solar panel will be
tested and the output voltage and current will be measured to confirm the correct
values are being produced. Next, the solar panel will be connected to depleted
backup battery UPS and the pair will be tested to confirm that the panel can fully
charge the battery from depletion in eight hours of less as calculated in the
research portion of this project. The battery’s output voltage and current will
finally be tested to ensure that values are safe for the electronics on the PCB.
Lastly, the secondary power system will be connected to the rest of the system
through the automatic power switch and the power switch will be tested. The
power switch will be tested by having the PCB run the custom OS will in the
middle of idling, the main power is pulled. If there is no interruption in power to
the system, the switch is deemed suiTable
Continuing, the audio section will be connected from the FPGA to the data pins
which lead directly to the DPSRAM. It will also be connected to the power
system. Next, an audio signal will be sent through the microphones and the
entire circuit will be tested to see if the processor receives the audio data from
each and every microphone with minimal distortion.
Finally, the system will be connected to the wireless bluetooth output. The
bluetooth output will first be tested for simple function to ensure that a bluetooth
signal is being sent successfully to another bluetooth device successfully. After,
the audio signals will be sent into the microphone array, and once the GLASS
software is running, if correctly implemented, the bluetooth will transmit the time,
114
location, and caliber of a gunshot to another bluetooth device that will feed the
data via wireless internet to the GLASS Android application where the data is
displayed. The bluetooth device that connects to the internet will be tested
simply on the basis if it will read in a bluetooth signal and transfer it to the
internet.
7.1.2 Current Design
First, the audio/microphone circuits were designed and soldered onto perfboards.
Each microphone was tested first by attaching the output of the microphones to
an oscilloscope where the waveforms were viewed. Once each microphone was
confirmed working the PCB was populated.
The GLASS PCB required large and small component population using both
hand soldering and hot plate soldering. PCB soldering is demonstrated in Fig.
7.1 below. Each lead on the PCB was electrically tested to insure the correct
voltage values. The PCB was then mounted inside of a birdhouse to represent
how inconspicuous GLASS can be. The microphone array was mounted on the
roof of the birdhouse.
Fig. 7.1- Hand Soldering GLASS PCB
Lastly, the wireless module used to communicate between the GLASS board and
the PC Arduino bridge was wired to the GLASS board and mounted to the
birdhouse as well.
115
7.2 Software
7.2.1– Initial Design
Although the software does not technically get fabricated, its development must
evolve as the fabrication of the system continues. While the first prototype is
being constructed, the first algorithms must be adapted to fit the final architecture
and then tested to ensure compatibility. Although the compiler will be able to
translate the c/c++ code to assembly, Linux will not know where in memory the
custom components reside, and it must therefore, be adapted to the to the new
architecture by modifying source code.
Testing for the software must occur at each stage of development. When the
Triangulation, and correlation algorithms are completing development, there will
be a phase of theoretical testing where prerecorded inputs are fed to the
algorithms to determine the reliability of the code. Special care must be taken to
replicate signals as they would appear During this testing phase the
communication with the Android device will also be experimented with, so that
the application on the Android device can be tested and finalized.
Once the prototype has been completed, the software must be made to conform
to the hardware of the GLASS system. This is a two step process beginning with
making the software run on the Beaglebone Black, to make the conversion to an
ARM environment, and then onto the hardware itself. The latter half of this stage
will be the most difficult because it will be the first opportunity to interface with the
DPSRAM and the four inputs for the microphones. We may begin to see timing
issues arise for the hardware resulting in changing the the rate at which the
microController accesses the buffers
The final move to the actual hardware should Involve few actual alterations to the
software. The primary objective at this stage is to optimize the code to GLASS’
architecture. When the move is made onto the final board connections between
components will shorten and may result in faster access times. This is also when
the field testing of GLASS commences. Multiple tests at varying ranges and
locations must be performed and this will be the first attempt to compensate for
such real world variables such as ambient noise, varying temperatures and the
introduction of echos.
116
6.2.2 – Current Design
Considering that the hardware specifications have been simplified, the current
design is only using two types of coding languages: C and C++. With the GLASS
PCB replacing the FPGA and ADCs in the audio block, it functions as the audio
listener and data transfer system. The GLASS board uses a modified C
programming compiled by Arduino software. This code listens for audio
signatures, captures them, and then transfers the data over Wi-fi to a secondary
Arduino connected to the PC. The secondary Arduino functions as a bridge
between the Wi-fi. The code utilizes the same peak detection algorithm as
previously mentioned in the initial design and the Wi-fi link is a simple data
transfer protocol.
In the current design, the main processing was moved from the GLASS board to a
main computer to handle the triangulation and gunshot recognition software.
Also, the Android app/device was removed and the computer is used for data
reviewing. The GLASS software is coded in an object oriented C++ language and
compiled in Microsoft Visual Studio.
The first thing the software does is create a data link between the computer and
the Arduino Uno via USB. The computer pulls the audio data being sent from the
GLASS board to the Arduino and brings it into the GLASS software for
processing. The software itself processes the data the exact same way that the
initial design section describes, using the same algorithms, equations, and theory.
The only difference is between the initial and current designs on this process is
where the processing is occurring.
After the location and gunshot are determined, the software then reports the data
on the computer screen for viewing. A timestamp from the computer is
associated with each gunshot and the location is output in relative terms to the
location of the GLASS microphone setup.
117
Chapter 8
Design Summary and Conclusion
GLASS is a system designed to locate, recognize, and alert gunshots. The
project has undergone a massive redesign for testing and presentation purposes
due to a restriction of resources. The initial design stands as a consumer end
product and the current design demonstrates a proof-of-concept for the original
design.
The initial design composed of two systems working together to process
information to be published to a user interface on a separate device. Audio is
sampled through a microphone array then the analog signal is converted into
digital before being buffered by four modules of DPSRAM via FPGA. The
processing occurs on a custom PCB running a dual core ARM® Cortex™A9. The
processed data is then sent to an Android enabled device for viewing via
Bluetooth.
The current design utilizes a custom PCB with an ATmega328 which listens and
forwards audio data via Wireless communication to a computer which will
process the data in the same fashion the original GLASS board would. The
output data is viewable on the computer in which does the processing in the
current design as well rather than an Android device.
In the processing phase of both designs, when a sample of sufficient amplitude is
received it processes the information as necessary to run the location and
correlation algorithms. Then the correlation and location algorithms are run to
determine the weapon type and the location where the gunshot came from.
The hardware for the main board was initially modeled after the BeagleBone
Black. This design utilized a main power source, a typical 120 V AC power outlet
and a secondary power source, an uninterrupted power supply battery charged
by a 30 W photovoltaic solar panel. The peripherals included GPS, Bluetooth
transceiver, USB, and an audio buffer. This design was not put into production
because of resource constraints and the current proof-of-concept design was
implemented.
118
Chapter 9
Administrative Content
9.1 Budget and Funding
GLASS is being graciously funded by both Boeing Co. All other costs above the
initial funding by Boeing Co. are being covered by the project engineers’ own
personal finances equally split. Figure 9.1 below shows a detailed breakdown of
the cost of parts for GLASS.
Part
Description
Part Number
Main ARM CPU
A9
MCIMX6D5EYM10AC
DPSRAM
CY7C026A
MT41J128M8JP-15E:G
Memory
TR
Power Supply 285-1417-ND
Custom CB
USB Port
USB
Flash
Drive
Network
Adapter
PV Panel
Battery
Microphone
Audio Wiring
Power Wiring
Basys
2
Spartan-3E
FPGA Board
2:1 Mux
Temp sensor
GPS
Bluetooth
MicroSD card
16 GB
MISC Shipping
Distributor
Qty Price
Total
Digi-key
Digi-key
1
4
39.9
36.80
39.9
147.20
2
1
4.26
18.18
8.52
18.18
N/A
2040451-1
Digi-key
Digi-key
PCBFabExpre
ss.com
Digi-key
1
2
100
1.16
100
2.32
DT101G2/8GBZET
Amazon
1
5.98
5.98
E261-5026
Instapark 30W PV Panel
CP1500AVRLCD
CMA-4544PF-W
MPC-35-2XRCA-25
YYPT-POWAKIT8
Tiger Direct
Amazon
Amazon
Digi-key
Mediabridge
Tiger Direct
1
1
1
10
1
1
14.99
84.50
159.99
0.746
11.99
17.21
14.99
84.50
159.99
7.46
11.99
17.21
Xilinx Spartan 3E FPGA,
-250 die
SN74AUP1T158DCKR
LM94022BIMGX/NOPB
A2235-H
PAN1721
Diligent
Digi-key
Digi-key
Mouser
Digi-key
1
2
1
1
1
69.00
.40
1.14
14.42
15.01
69.00
.80
1.14
14.42
15.01
DV7834
Amazon
1
6.19
6.19
75.80+ 75.80+
Total: 800.00
119
9.2 Planning and Milestones
Milestones
Start Date
End Date
Duration
(Days)
Research Phase
9/9/2013
10/14/2013
35
Group Formation
9/9/2013
9/16/2013
7
Task Division
9/16/2013
9/23/2013
7
Triangulation Algorithm 9/16/2013
10/7/2013
21
Event Recognition
9/16/2013
9/30/2013
14
Microphone Array
9/23/2013
10/7/2013
14
Hardware Design
9/16/2013
10/7/2013
21
PV Integration
9/23/2013
10/7/2013
14
Network Interface
9/16/2013
10/7/2013
21
Embedded Linux
9/16/2013
10/7/2013
21
Software-Hardware
Interface
9/23/2013
10/14/2013
21
Design Phase
10/7/2013
12/1/2013
55
PCB
10/7/2013
11/11/2013
35
Algorithm
Implementation
10/7/2013
11/18/2013
42
Software Development 10/14/2013
12/1/2013
48
Hardware/Sensor
Interface
10/28/2013
12/1/2013
34
Implementation
Phase
1/6/2014
4/28/2014
112
Test Microcontroller &
PCB
1/6/2014
2/3/2014
28
Test
Software
Comm.
2/3/2014
28
Test & Debug System 2/3/2014
4/14/2014
70
Finalize
Present
4/28/2014
14
&
1/6/2014
Project/
4/14/2014
120
9.3 Management Style
The GLASS project utilized a hybrid of Agile and Democratic management styles.
It was decided early in the project team formation that there would be no single
member managing every other team members’ work. However as the project
progressed, the team members have formed divisions of expertise as the tasks
with interrelated components gained in complexity. For example if a portion of the
project interrelates two disparate components through a central component, the
team member who works on that part of the subsystem must be kept aware of
the other two.
Starting with the creation of the project topic, the team’s engineers had Round
Table Meetings to discuss and informally vote on which project topics were good
and which were subpar. This democratic process continued through the division
of labour process where the team shared their strengths and weaknesses, and
the project tasks were quickly assigned. Some members shared strengths and
therefore some of the project tasks were split equally. The democratic
management style worked very well with a team size this small and there was
never any long term productivity freezes due to disagreeing team members.
After the project topic was decided and division of labour completed the project
research and design phase was loosely based on SCRUM management.
SCRUM management is defined by Forbes as having the following ten
characteristics:
1. Organize work in short cycles.
2. The management does not interrupt the team during a work cycle.
3. The team reports to the client, not the manager.
4. The team estimates how much time work will take.
5. The team decides how much work it can do in an iteration.
6. The team decides how to do the work in the iteration.
7. The team measures its own performance.
8. Define work goals before each cycle starts.
9. Define work goals through user stories.
10. Systematically remove impediments.
GLASS’ team of engineers met as a whole at least biweekly to brainstorm,
discuss, plan, design, and asses past, current, and future work. In general, no
member of the team interrupted another team member’s work cycle playing a
managerial role (2). Although, there was no client, the team reported to itself and
not to a manager (3) and measured its performance at each meeting (7).
As shown in Figure 3.2, the Milestone table, the work loads were organized in
short work cycles (1), decided how much time the work would take (4), how much
work to do in each iteration (5), and defined work goals before each cycle (8).
Each member of the team decided how to do his work or worked with another
121
team member for splitting work (6). Each team member discussed their efforts
since the last meeting (9) and through team member support, the group quickly
dissipated any problems that occurred.
The Democratic SCRUM management style definitely worked well for Team
GLASS as it produced reliable and consistent work flow and results. The
collaboration continued as each team member’s results started to be recorded.
To keep track of the data, all member files were kept in a shared Google Docs
folder where each team member could make comments on documents, update
data, and type reports real time. So although the team only met biweekly, with
the real time collaboration on GoogleDocs, the team met multiple times a week.
122
9.4 Division of Labour
In general, the electrical engineering majors: Babamir and Kon are mostly
responsible for the hardware and signal processing designs where the computer
engineering majors: Salazar and Alvarado are mostly responsible for the
software and integration of the software on the hardware built by Babamir and
Kon. Figure 9.1 below demonstrates a basic division of labour in the GLASS
project.
Figure 9.1: Distribution of Workload
123
Acknowledgments
At the beginning of this project, it was clear that the financial burden would not be
light due to the large amount of components required and the complex
technology needed to implement the GLASS design. Therefore, we would first
like to thank our main financial sponsor, The Boeing Company for funding
GLASS. Boeing has allotted financial support for projects in the categories of
homeland and cyber security.
The GLASS team would like to extend our gratitude to Sunstone Circuits for
providing our hardware designers with priceless advice on printed circuit board
design as well as providing us with a discount on all of our printed circuit boards
through their sponsorship program. Christian Kon would personally like to thank
Erik Torell from Sunstone Circuits for aiding with a double check of the GLASS
board schematic.
Third, we thank Maxim Integrated for sending us samples of components
essential to our initial design as well as providing us with countless resources on
audio signal processing related to their products and great customer service.
Next, we appreciate all of the professors that guided and mentored us to success
in this project. And we are very gracious to NRA Licensed Instructor and Range
Safety Officer John Caballero for not only providing us with sponsored range time
at the Central Florida Rifle and Pistol Club, but for also challenging our designers
with great mentoring on sound and waveform analysis relating to firearm sound
analysis and identification.
Lastly, we extend our thanks and love to our families and friends for dealing with
many sleepless nights, bad moods, and events missed necessary for GLASS to
succeed.
Sponsored By
124
Appendix A
Copyright Requests and Licensing
125
126
Figure 3.15, credited to the author “Jephthai” who has released it under Creative
Commons Attribution 3.0. http://commons.wikimedia.org/wiki/File:Ruger-LCR-38sp-front-quarter.jpg
Figure 3.19, credited to the author “Vladmir Dudak” who has released it under
Creative Commons Attribution 3.0.
http://en.wikipedia.org/wiki/File:GLOCK_19.JPG
Figure 3.22, credited to the author “Sam Lisker” who has released it under
Creative Commons Attribution-Share Alike 3.0.
http://commons.wikimedia.org/wiki/File:Colt_Model_of_1911_U.S._Army_b.jpg
Figure 3.27, credited to the author “Dictouray” who has released it under Creative
Commons Attribution-Share Alike 3.0.
http://en.wikipedia.org/wiki/File:Ruger-1022.jpg
Figure 3.31, credited to the author “Steelerdon” who has released it under
Creative Commons Attribution 3.0.
http://en.wikipedia.org/wiki/File:1973_Colt_AR15_SP1.jpg
Figure 3.34, image is part of the public domain.
http://en.wikipedia.org/wiki/File:Rifle_Springfield_M1903.jpg
Figure 3.37, image is part of the public domain.
http://commons.wikimedia.org/wiki/File:Remington_M870_12_Gauge.jpg
127
Appendix B
Works Cited
[1] “Shotgun detector”. Armada.ch. Web.
http://www.armada.ch/gunfire-location-system-acoustic-gunshot-detectionsystems/
[2] “Gunshot locater system”. Wired. Web.
http://www.wired.com/dangerroom/2010/03/military-helicopters-may-get-gunshotlocation-system/
[3] “Gunshot locater system”. Bbn. Web.
http://bbn.com/boomerang
[4] “Gunshot locater system”. Bbn. Web.
http://bbn.com/boomerang
[5] “How a microphone works”. Audiolinks. Web.
https://microphones.audiolinks.com/microphones.shtml
[6] “How wavelets work”. Tscm. Web.
http://www.tscm.com/wavelet.pdf
[7] “Audio shotgun detection”. Colostate.edu. Web.
http://digitool.library.colostate.edu///exlibris/dtl/d3_1/apache_media/L2V4bGlicmlz
L2R0bC9kM18xL2FwYWNoZV9tZWRpYS8xNzUzODI=.pdf
[8] Donzier, A., Cadavid, S., Small arm fire acoustic detection and localization
systems: gunfire
detection system, Proc. SPIE Vol. 5778, p. 245–253, Jun 2005.
128

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