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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

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


talk outline
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
problem solution
  • 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
pinptr system architecture
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
custom sensor board and mica2 mote
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
ms time synchronization
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
ms time synchronization9
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
ms message routing
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
ms sensor localization
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
ms sensor localization cont
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.
signal detection
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
sensor fusion
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
performance results
Performance Results
  • Experimental setup
    • 56 motes
    • 20 different known shooter positions were used
    • 171 shots were fired
performance results17
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
performance results18
Performance Results
  • Sensor Density
    • Effects signal detection
    • Increases shooter localization error
performance results19
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
future work and conclusion
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
limitations discussion
  • 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