Detection, Classification and Tracking in Distributed Sensor Networks

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Detection, Classification and Tracking in
Distributed Sensor Networks
D. Li, K. Wong, Y. Hu and A. M. Sayeed
Dept. of Electrical Computer
Engineering University of
Wisconsin-Madison Akbar_at_engr.wisc.edu
http//dune.ece.wisc.edu/
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Overview

• -Small, densely distributed wireless sensors.
• -Collaboration necessary for tracking and
  classification, but not for detection.
• -Multi-modal sensors (potentially)
• -Tracking vehicles (tanks, trucks).

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Space-Time Sampling

• Sensors sample the spatial signal field in a
  particular modality (e.g., acoustic)
• Sensor density commensurate with spatial signal
  variation
• Sampling of time series from each sensor
  commensurate with signal bandwidth
• Signal field decomposed into space-time cells to
  enable distributed signal processing

Space
Space
Time
Time
Uniform space-time cells
Non-uniform space-time cells
A moving object corresponds to a spatial peak
moving with time Target tracking corresponds to
determining the peak location over time
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Detection

• Simple energy detector
• detect a target/event when the output exceeds a
  threshold
• Otherwise, update the threshold.
• Detector output
• at any instant is the average energy in a
  certain window is sampled at a certain rate based
  on a priori estimate of target velocity

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Target Localization at a Time Instant

• CPA/Energy based
• Location of the sensor with largest output
• Using attenuation exponent and 4 or more sensor
  measurements.

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Single Target Tracking

• Initialization Put all locations where vehicle
  can enter (in this case the corners) onto
  detection alert.
• Three-step procedure
• A track is initiated when a target is detected in
  a region
• Target locations are estimated for N successive
  time instants. These positions are used to
  predict target location at MltN future instants
• Predicted positions are used to create new
  regions that are put on detection alert

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Multiple Targets

• Can track multiple targets if they are
  sufficiently separated in space and/or time
• separate track for each target
• Can track spatio-temporally overlapping targets
  with appropriate classification algorithms

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Classification

• Three types of classifiers under investigation
• K-nearest neighbor (KNN)
• Maximum likelihood (ML) (Gaussian mixture)
• Support Vector Machine (SVM)
• Three target classes
• Wheeled
• Tracked
• Unknown

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Some Issues and Challenges

• Variability in measurements and conditions
• Have to rely on data from prior experiments to
  train the classifiers for new experiments
• Variations in spectral signatures due to motion
  (Doppler), acceleration, gear shifts
• Timing synchronization for collaborative
  processing

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Summary

• Framework for detection, classification and
  tracking of targets
• Single-node algorithms
• Energy detection
• Classification (of single target events)
• Collaborative Processing
• Localization
• Location prediction for tracking