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Shooter Localization with Wireless Sensor Networks

Shooter Localization with Wireless Sensor Networks. Akos Ledeczi Associate Professor akos.ledeczi@vanderbilt.edu. Many single-channel acoustic sensors 2003-2005 Designed for urban operation: Multipath elimination Multiple simultaneous shot resolution 1-meter 3D accuracy within network

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Shooter Localization with Wireless Sensor Networks

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  1. Shooter LocalizationwithWireless Sensor Networks Akos Ledeczi Associate Professor akos.ledeczi@vanderbilt.edu

  2. 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 Evolution

  3. Wireless Sensor Network-Based Countersniper System

  4. event Tevent time node1 Δt1 time node2 Δt2 time node3 Δt3 time sink sink 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 • ~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. Sensor Fusion Shot #1 @ (x1,y1,T1) t3 d3 Shot #2 @ (x2,y2,T2) f(x,y) d1 ? t1 Echo #1 @ (x3,y3,T1) d4 t4 d2 t2 t4 – d4/v t1 – d1/v t2 – d2/v sliding window t3 – d3/v time 3 0 1 Shot time estimate T f(x,y) = [max number of ticks in window] = 3

  6. B1 Church Experiments at McKenna MOUT site at Ft. Benning • 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. VIDEO Shooter Localization

  10. 3-axis compass Optional laptop display Microphones Zigbee & Bluetooth Bluetooth PDA display Zigbee Zigbee Bluetooth Soldier-Wearable Shooter Localization System DARPA IPTO ASSIST 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. 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. VIDEO Test in Georgia in 2009

  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 PhonesDARPA 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. Network Phone Shockwave Muzzle blast Network Phone

  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. Phone Shooter 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 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. 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? .M2 .M2 • 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) .M1 .M1 .M2 .M1 .M3 .M3 .M3

  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 • 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 M240 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 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. More information: akos.ledeczi@vanderbilt.edu Questions? 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. 153-177, November, 2005

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