1. Shooter Localization with Wireless Sensor Networks
Akos Ledeczi
Associate Professor

2. Evolution Many single-channel acoustic sensors
Designed for urban operation:
Multipath elimination
Multiple simultaneous shot resolution
1-meter 3D accuracy within network
No classification
DARPA NEST Program
Few multi-channel acoustic sensors
Helmet-mounted, 4-channel acoustic sensor node
Single sensor operation: localization
Networked operation: trajectory and caliber estimation and weapon classification
1-degree bearing accuracy
DARPA ASSIST Program
Few single-channel acoustic sensors
2010-
Mobile phone-based system
Single sensor operation: miss distance and range estimation*
DARPA Transformative Applications Program

3. Wireless Sensor Network-Based
Countersniper System

4. RITS: Routing Integrated Time Synch
reactive protocol, synchronizes after the event was registered (post-facto)
maintains the age of event instead of the global time and computes the local time of event at the data fusion node
power efficient, virtually no communication overhead, can be highly accurate
node1
time
node2
node3
sink
Tevent
Δt1
Δt2
Δt3
event
sink
~50 node experiment
4.4 μs average error, 74 μs maximum error in the test of 200 rounds
Troot
Δt1 + Δt2 + Δt3
Tevent = Troot - Δt1 - Δt2 - Δt3

5. f(x,y) = [max number of ticks in window] = 3
Sensor Fusion
Shot (x1,y1,T1)
Shot (x2,y2,T2)
Echo (x3,y3,T1) t2 t1 t4 t3 d1
f(x,y) ? d3 d4 d2
time
t2 – d2/v
t3 – d3/v
t1 – d1/v
t4 – d4/v
f(x,y) = [max number of ticks in window] = 3
Shot time estimate T 3 1
sliding window

6. Experiments at McKenna MOUT site at Ft. Benning
Church
Sep 2003: Baseline system
Apr 2004: Multishot resolution
60 motes covered a 100x40m area
Network diameter: ~7 hops
Used blanks and Short Range Training Ammunition (SRTA)
Hundreds of shots fired from ~40 different locations
Single shooter, operating in semiautomatic and burst mode in 2003
Up to four shooters and up to 10 shots per second in 2004
M-16, M-4, no sniper rifle
Variety of shooter locations (bell tower, inside buildings/windows, behind mailbox, behind car, …) chosen to absorb acoustic energy, have limited line of sight on sensor networks
1 meter average 3D accuracy (0.6m in 2D)
Hand placed motes on surveyed points (sensor localization accuracy: ~ 0.3m)
NORTH

7. 2.5D Display, Single shot

8. 2.5D Display, Multiple Shots
Red circle:
Shooter position
White dot:
Sensor node
Small blue dot:
Sensor Node that detected current shot
Cyan circle:
Sensor Node whose data was used in localization
Yellow Area:
 Uncertainty

9. Shooter Localization
VIDEO

10. Soldier-Wearable Shooter Localization System DARPA IPTO ASSIST
Zigbee
&
Bluetooth
Microphones
3-axis compass
Optional
laptop display
PDA display
Zigbee
Bluetooth
Shockwave
Muzzle blast

11. Acoustic Sensor Board
Detect TOA and AOA of ballistic shockwave and muzzle blast using a single board
Acoustic sensor board:
4 acoustic channels w/ high-speed AD converters
FPGA for signal processing
3-axis digital compass
Bluetooth
MicaZ connectivity

12. Software Architecture
PC/PDA (Java/Ewe)
User interface
Local/central sensor fusion
Location information from external GPS
Sensor Board (VHDL/assembly)
Custom DSP IP cores (detection)
Soft processor macros (digital compass, debug & test interface)
Communication bridge
Shared memory paradigm
Mote (nesC/TinyOS):
Data sharing across nodes
Time synchronization
Application Configuration & Management (from a central point)

13. Single Sensor Results
Independent evaluation by NIST at Aberdeen in 2006
Localization rate for single sensors:
range < 150m: 42%
Range < 80m: 61%
Percentage of shots not localized by at least one single sensor alone (range < 150m): 13%
Accuracy:
0.9 degree in azimuth
5 m in range
Blue dots: sensors
Black squares: targets
Black line: trajectory estimate
Black dot: shooter position estimate
White arrows: single sensor shooter estimates

14. Sensor Fusion
Localization: Single sensor: simple analytical formula to compute shooter location based on Time of Arrival (ToA) and Angle of Arrival (AoA) of both shockwave and muzzle blast.
Localization: Multi-sensor: all available detections are utilized in a multiresolution search of a discrete multi-dimensional consistency function. Consistency function specifies how many observations agree on a given point in space and time.
Online caliber estimation based on measured ballistic shockwave length and miss distance given by the computed trajectory estimate.
Online weapon classification based on estimated caliber and muzzle velocity that is computed using the projectile velocity over the sensor web and the estimated range.

15. Classification Results
Multi-Sensor Results
Independent evaluation by NIST at Aberdeen in 2006
Shots between 50 and 300m w/ 6 different weapons (3 calibers)
Trajectory was highly accurate
Big range error at >200m was due to a bug in the muzzle blast detection
Caliber estimation was almost perfect (rates are relative to localized shots, not all shots).
Classification for 4 out of 6 six weapons were excellent
At longer ranges it started to degrade as it needs range estimate, i.e. muzzle blast detections
M4 and M249 was too similar to each other and the test was the first time the system encountered these weapons
Localization Results
Classification Results
Sensors located on surveyed points with small position error.
Manual orientation and then automatic calibration used. No mobility.

16. Test in Georgia in 2009
VIDEO

17. Motivation for New Approach
Traditional WSN approach:
Many single channel sensors distributed in the environment
Too many nodes needed
Wearable sensor approach:
Few multi-channel sensors
Needs to track self-orientation: Hard!
What can be done with a few single-channel sensors?

18. SOLOMON: Shooter Localization with Mobile Phones DARPA Transformative Apps Program
Accurate miss distance estimation using a single microphone (i.e. phone) by measuring the shockwave length. Estimated accuracy: 1-2m.
Accurate range estimation using a single microphone (i.e. phone) utilizing the miss distance and the TDOA of the shockwave and the muzzle blast: Estimated accuracy: 5%.
Novel consistency function-based sensor fusion technique enables localization of shooter with as few as 5 phones even in the presence of GPS and other errors.
Custom headset will provide better performance offloading the computationally intensive operations from the phone increasing battery life.
Muzzle blast
Shockwave
Phone
Network

19. Miss Distance Estimation in Standalone Operation
Relation between shockwave length (N-wave duration in the time domain) and miss distance [Whitham52]:
T: shockwave length
M: Mach speed of the bullet
b: miss distance
c: speed of sound
d: bullet caliber
l: bullet length
Miss distance can be computed from the shockwave length, with assumptions on the weapon (caliber, length and speed of bullet):
b: miss distance
T: shockwave length
k: weapon coefficient
Using 168 shockwave detections of AK-47 shots fired from 50 to 130m from sensors, with miss distances ranging from 0 to 28m, the average absolute miss distance error is 1m.

20. Range Estimation in Standalone Operation
Range can be calculated using the miss distance, a projectile speed and the TDOA of the shockwave and the muzzle blast.
SM:
QM: P:
SP:
PM: α:
range
miss distance
origin of shockwave heard at M
at the speed of bullet
at the speed of sound
shockwave cone angle
Phone
Shooter
Using 168 AK-47 shot detections from ranges between 50 and 130 m gathered at Aberdeen in 2006 the average range estimate has ~5% error.

21. Custom Headset
High quality application-specific microphone with higher maximum sound pressure and faster recovery (Knowles VEK-H-30108)
Higher sampling rate for better shockwave length and miss distance estimation
Off-loading the signal processing algorithm from the phone using a low-power ARM-Cortex microcontroller
real-time signal processing with lower jitter and latency
better performance/power ratio
Wired and/or wireless phone interface supporting any Android handset device
Bluetooth interface with Android 2.0 and later
Analog signaling on the headset audio interface using software modems on both sides
Integrated temperature sensor for more accurate speed of sound estimation

22. No known weapon assumption.
Networked Operation
Multilateration: find an initial shooter position estimate using muzzle blast TDOAs
optional
Trajectory search: minimize an error function in a predefined search space
Inputs:
shockwave TDOAs
shockwave length
Optimized parameters:
trajectory
weapon coefficient
Side effects:
Bullet speed is computed
Miss distances are available
Final shooter localization: constrained triangulation using range estimates
Weapon classification using weapon coefficient and bullet speed
No known weapon assumption.

23. Error Function: Miss distance consistency
Optimize the weapon coefficient for the trajectory
What is the best weapon coefficient for the evaluated trajectory?
How good is the match?
Which trajectory has the best match?
.M1
.M2
.M3
.M1
.M2
.M3
.M1
.M2
.M3
Miss distance is proportional to the fourth power of the shockwave length.
Miss distance is linearly related to weapon coefficient.
MSE of the n best miss distances is used as a metric for the trajectory (n=5 is good in practice)

24. Error function: Cone angle consistency
Pairwise shockwave TDOA-based trajectory angle consistency
Given a trajectory, the shockwave TDOA of two nodes can be used to compute the shockwave cone angle.
We compute the shockwave cone angle for all pairs of nodes, and use the variance of the most consistent subset of size n as the metric (n=5 is good in practice).
Mi: microphone i position
Bi: position of bullet when shockwave reaches microphone i
Qi: point on trajectory closest to microphone i
bi: miss distance
c: speed of sound
α: shockwave cone angle
Δt: shockwave TDOA
The multiple of the miss distance-based and the cone angle-based consistency metric is minimized.

25. Final Shooter Localization
Trajectory is known at this point
Miss distances are also known
Bullet speed is also known
Range to each sensor can be estimated without the known weapon assumption!
Constrained trilateration using ranges and the known trajectory
Multilateration
Trilateration
Composite

26. Classification 12.70mm M107 7.62mm M240 AK47 M16 5.56mm M4 & M249
Based on weapon coefficient and projectile speed, the bullet coefficient (caliber and length) is estimated
Based on bullet coefficient, range and speed, the muzzle velocity can be estimated (using an approximate deceleration profile)
Caliber and muzzle velocity is characteristic of rifles
12.70mm
M107
7.62mm
T: shockwave length
v: bullet speed (over network)
M: Mach speed of the bullet
b: miss distance
c: speed of sound
d: bullet caliber
l: bullet length
AK47
M240
M16
5.56mm
M4 & M249

27. Evaluation
Out of 108 shots, 107 trajectories could be computed. Average trajectory angle error is 0.1 degree, with standard deviation of 1.3 degrees. Absolute trajectory angle error is 0.8 degree.
Out of 108 shots, 104 shooter positions could be computed. Average position error is 2.96m, which is better than the 5.45m error with the previous, multi-channel system.

28. Results from a single soldier’s POV
Average individual bearing error is 0.75 degree.
Average range error is 0.2m, with standard deviation of 3.3m. Average absolute range error is 2.3m.

29. Questions? More information: akos.ledeczi@vanderbilt.edu
Sallai, J., Ledeczi, A., Volgyesi, P.: “Acoustic Shooter Localization with a Minimal Number of
Single-Channel Wireless Sensor Nodes” SenSys 2011
Volgyesi, P., Balogh, G., Nadas, A, Nash, C., Ledeczi, A.: “Shooter Localization and Weapon Classification with Soldier-Wearable Networked Sensors” MobiSys 2007
Ledeczi, A. et al.: “Countersniper System for Urban Warfare,” ACM Transactions on Sensor Networks, Vol. 1, No. 2, pp , November, 2005