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Sensor Network-Based Countersniper System. Gyula S, Gyorgy B, Gabor P, Miklos M, Branislav K, Janos S, Akos L, Andras N, Ken F Presenter Yamuna Krishnamurthy 2/7/05. Talk Outline. Problem/Solution PinPtr System Architecture Middleware Services Time Synchronization Message Routing

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Sensor Network-Based Countersniper System

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Sensor network based countersniper system l.jpg

Sensor Network-Based Countersniper System

Gyula S, Gyorgy B, Gabor P, Miklos M, Branislav K, Janos S, Akos L, Andras N, Ken F


Yamuna Krishnamurthy


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Talk Outline

  • Problem/Solution

  • PinPtr

    • System Architecture

    • Middleware Services

      • Time Synchronization

      • Message Routing

      • Sensor Localization

    • Signal Detection

    • Sensor Fusion

      • Consistency Function

      • Search Algorithm

    • Performance Results

  • Future Work and Conclusion

  • Limitations/Discussion

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  • Solution

    • Use an ad-hoc wireless sensor network-based system

    • Utilize many cheap sensors for

      • good coverage of direct signal

      • tolerance to failures

    • Detect via acoustic signals like muzzle blasts and shockwaves

  • Problem

    • Locate snipers in urban environments

    • Work with constraints of the urban environment

      • Multipath effects

      • Poor coverage due to shading effect of buildings

    • Overcome limitations of existing systems

      • Require direct line of sight

      • Rely on muzzle flash that can be suppressed

      • Centralized system not tolerant to sensor failure

    • Cost effectiveness

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PinPtr - System Architecture

  • Ad-hoc wireless network of inexpensive sensors

  • Sensors can

    • detect muzzle blasts and acoustic shockwaves

    • Measure their time of arrival (TOA)

  • Message routing service delivers TOA to a base station

  • User Interface through base stations or PDAs

  • System field tested at the US Army McKenna MOUT (Military Operations in Urban Terrain) facility at Fort Nenning, GA

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Custom Sensor Board and Mica2 Mote

PinPtr – System Architecture

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Custom Sensor Board and Mica2 Mote

PinPtr Application


  • Time synchronization

  • Message routing

  • Data aggregation

Operating System

  • Tiny OS (UC Berkeley)

    • Task scheduling

    • Radio communication

    • Clocks and timers

  • I/O

  • Power management


  • Mica Mote

    • Microcontroller

    • Multichannel receiver

    • 4KB RAM, 128KB Mem

    • Extension Interface

  • Acoustic Sensor Board

    • 3 acouastic channels

    • FPGA

      • Signal Processing

      • Measure TOA

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Middleware Services (MS)

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MS - Time Synchronization

  • Flooding Time Synchronization Protocol (FTSP)

    • Synchronize local clock to clock of selected root node

    • Time stamping broadcasted radio message multiple times at sender and receiver nodes

    • Time stamps made when sending/receiving individual bytes

    • Reduce uncertainties of encoding/decoding and interrupt handling times

    • Final error corrected value embedded into message before end of transmission

    • Estimate global time by synchronizing with nodes a level above

    • Less communication overhead

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MS - Time Synchronization

  • Alternate algorithm

    • Power conservation

    • Does not require continuous radio broadcast to synch time

    • Use data packets for time synch

    • Each node adds an age = (prev age) + (time pkt resent – time pkt received)

    • Time of event = T(recv) - age

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MS - Message Routing

  • Gradient-Based best effort converge-cast protocol

    • Assign a root node

    • Route data from all nodes to the root node

    • Each node rebroadcasts data packets upto 3 times

    • Data packets reach the root through multiple paths

    • Fast and robust

    • Does not guarantee message delivery

    • Has significant message overhead

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PinPtr – System Architecture

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MS - Sensor Localization

  • Self-Localization procedure using ranging procedure

    • Ranging procedure

      • Broadcast a radio message followed by multiple chirps

      • Destination node samples each chirp by streaming microphone

      • Adds the samples together to increase signal to noise ratio

      • After recording, a digital band pass filter and peak detector estimate start of the first chirp

      • Range is computed using time of flight of the chirp

    • Assign unique time slots to adjacent nodes within two hops radius

    • In each time slot acoustic ranging procedure is initiated between the node and its neighbors

    • Measurements are propagated to the base

    • Base estimates the relative position of node to known anchor points through optimization procedures

    • Advantage

      • Reconfigure dynamically when sensors fail and new sensors are added

    • Disadvantages

      • Requirement of all nodes having 4 neighbors within 10mts range is not practical

      • Sounder makes sensors larger and consumes more power

      • Audible frequency of sounder makes detection by adversary easy

      • Ultrasonic sounders have lesser range

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MS - Sensor Localization – Cont.

  • Passive acoustic sensor localization

    • Use external acoustic sources

    • In sniper scenario estimate sensor location through shots rather than sniper location with sensors

    • Produce 6 shots at known locations at unknown times and locate 4 sensors using linearization

      • Requires sensor to be in direct line of sight

      • Sensitive to small individual measurement errors

      • Non- analytic approach too slow

    • Produce shots at known locations and known times

      • Same as the active acoustic method

  • PinPtr Methodology

    • Due to shortcomings of above mentioned localization procedures PinPtr currently uses sensors at known locations.

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Signal Detection

  • Incoming acoustic signal samples at 1MHz

  • Compressed using Zero Crossing (ZC) coding

  • Interval between ZCs is coded by storing

    • Start time of the interval (T)

    • Length of the interval (L)

    • Minimum or maximum signal value (Mm)

    • Previous signal avg amplitude (P)

    • Rise time (Γ)

  • Detect muzzle blast patters in the coded stream

  • Modeled with state machine states IDLE, POSSIBLE_START, DETECTED

  • When DETECTED start time at POSSIBLE_START is returned as TOA

  • Mote transmits TOA to base stations

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Sensor Fusion

  • Converge on the actual shooter position from positions calculated with TOA

  • Consistency Function using 3D space and time

    • Known values

      • Location (xi,yi,zi) of sensor making i th measurement

      • ti the time of arrival of detected muzzle blast

    • CΓ(x,y,z,t)= count (|ti(x,y,z,t)-ti| <= Γ)


  • Search algorithm for convergence

    • Generalized bisection based on interval arithmetic

      • Create 4-dimensional spaces with the intervals [xmin,xmax]x [ymin,ymax]x [zmin,zmax]x [tmin,tmax]

      • Determine CΓfor each of the spaces

      • Select the area with CΓmax

      • Bisect the area along the longest dimension and repeat from 1-4 till maximum region is less than vΓ/2 for space and Γ/2 for time

      • Shooter position falls in this max area

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Performance Results

  • Experimental setup

    • 56 motes

    • 20 different known shooter positions were used

    • 171 shots were fired

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Performance Results

  • Shooter localization error

    • Elevation info eliminated for 2D

    • 3D errors are more as sensors were mostly positioned on the ground

  • Error Sources

    • Time Synchronization errors

    • Sensor localization errors

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Performance Results

  • Sensor Density

    • Effects signal detection

    • Increases shooter localization error

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Performance Results

  • Sensor Fusion

    • Compare analytic solution to the fusion algorithm

    • Analytic solution compares to fusion algorithm when no error readings are included

    • With error readings fusion algorithm provides a much better solution

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Future Work and Conclusion

  • Future Work

    • Provide power management

    • Use shockwave signal

    • Support multiple shots with sensor fusion algorithm

    • Use post-facto time synchronization to conserve power

    • Use system in other Concepts of Operations

      • Reconnaissance missions

      • Protect convoy routes

    • Work on sensor self-localization techniques

  • Conclusion

    • PinPtr provides a counter-sniper system

    • Provides efficient algorithms for time synchronization and shooter/sensor localization

    • Good experiment to reassure actual deployment

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  • Since PinPtr does not employ shockwave signals and relies on muzzle blast it may not work when silencers are used

  • Deployment of sensors in actual urban environment like NY is not trivial

  • Does not provide for power conservation hence battery life can be an issue since these systems typically need to be available always

  • Cannot deal with multiple shots fired by multiple snipers

  • No self-localization performed hence cannot dynamically configure with change in number of sensors

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Thank You

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