Shooter Localization with Wireless Sensor Networks

Institute for Software Integrated SystemsVanderbilt University

Shooter Localizationwith

Wireless Sensor Networks

Akos LedecziAssociate Professor

[email protected]

Copyright © 2004-2011, Vanderbilt University2

Evolution Many single-channel acoustic sensors

2003-2005 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 2005-2006 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* Networked operation: trajectory and caliber estimation and weapon classification DARPA Transformative Applications Program


Wireless Sensor Network-BasedCountersniper System


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

Δt1 + Δt2 + Δt3 Troot

Tevent = Troot - Δt1 - Δt2 - Δt3

node1time

node2time

node3time

sinktime

Tevent

Δt1

Δt2

Δt3

event

sink

average time synchronization error histogram

0%

5%

10%

15%

20%

25%

30%

0 2 3 5 6 8 10 11 13

synchronization error (microseconds)

perc

enta

ge

- ~50 node experiment- 4.4 μs average error, 74 μs maximum error

in the test of 200 rounds

Copyright © 2004-2011, Vanderbilt University 5

t2

t1

t4

t3

d1

f(x,y)?

d3

d4

d2

Shot #1 @ (x1,y1,T1)

Shot #2 @ (x2,y2,T2)

Echo #1 @ (x3,y3,T1)

timet2 – d2/vt3 – d3/v

t1 – d1/vt4 – d4/v

f(x,y) = [max number of ticks in window] = 3Shot time estimate T

3 0 1

sliding window

Sensor Fusion


Experiments at McKenna MOUT site at Ft. Benning

NORTH

B1Church

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)


2.5D Display, Single shotRed 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


2.5D Display, Multiple ShotsRed 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


Shooter Localization

VIDEO

Copyright © 2004-2011, Vanderbilt University

Soldier-Wearable Shooter Localization System

DARPA IPTO ASSIST

Muzzle blast Shockwave

Zigbee&

Bluetooth

Microphones

3-axis compassOptional

laptop display

PDA displayZigbee

Bluetooth

Bluetooth

Zigbee


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


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)


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: sensorsBlack squares: targetsBlack line: trajectory estimateBlack dot: shooter position estimateWhite arrows: single sensor shooter estimates

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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.

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Multi-Sensor Results

Localization Results

Classification 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

Sensors located on surveyed points with small position error. Manual orientation and then automatic calibration used. No mobility. 15


Test in Georgia in 2009

VIDEO


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!

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What can be done with a few single-channel sensors?


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.

SOLOMON: Shooter Localization with Mobile Phones

DARPA Transformative Apps Program

Muzzle blast Shockwave

Phone Network

Phone Network

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Relation between shockwave length (N-wave duration in the time domain) and miss distance [Whitham52]:

Miss Distance Estimation in Standalone Operation

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T: shockwave lengthM: Mach speed of the bulletb: miss distancec: speed of soundd: bullet caliberl: bullet length

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.

Miss distance can be computed from the shockwave length, with assumptions on the weapon (caliber, length and speed of bullet):

b: miss distanceT: shockwave lengthk: weapon coefficient


Range can be calculated using the miss distance, a projectile speed and the TDOA of the shockwave and the muzzle blast.

Range Estimation in Standalone Operation

Phone

Shooter

SM:QM:P:SP:PM:α:

rangemiss distanceorigin of shockwave heard at Mat the speed of bulletat the speed of soundshockwave cone angle

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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.


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

Custom Headset

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Networked Operation1. Multilateration: find an initial shooter position estimate using muzzle

blast TDOAs optional

2. 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 speed22

No known weapon assumption.


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)

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


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).

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Mi: microphone i positionBi: position of bullet when shockwave reaches microphone iQi: point on trajectory closest to

microphone ibi: miss distancec: speed of soundα: shockwave cone angleΔt: shockwave TDOA

The multiple of the miss distance-based and the cone angle-based consistency metric is minimized.


Final Shooter Localization

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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


Classification 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

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T: shockwave lengthv: bullet speed (over network)M: Mach speed of the bulletb: miss distancec: speed of soundd: bullet caliberl: bullet length

12.70mm

7.62mm

5.56mm

M107

M240AK47

M4 & M249

M16


Evaluation

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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.


Results from a single soldier’s POV

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Average range error is 0.2m, with standard deviation of 3.3m. Average absolute range error is 2.3m.

Average individual bearing error is 0.75 degree.


Questions?

More information:

[email protected]

Sallai, J., Ledeczi, A., Volgyesi, P.: “Acoustic Shooter Localization with a Minimal Number ofSingle-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. 153-177, November, 2005

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Shooter Localization with Wireless Sensor Networks

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