Detection, Classification and Tracking of Targets in Distributed Sensor Networks

1
Detection, Classification and Tracking of Targets
in Distributed Sensor Networks
Dan Li, Kerry Wong, Yu. H. Hu, and Akbar M. Sayeed
Presented by Prabal Dutta prabal_at_eecs
2
Outline of the Talk

• Introduction
• Signal Processing Primitives
• Tracking
• Target Classification
• Issues and Challenges
• Future Research
• Conclusions
• Remarks
• Discussion

3
Introduction

• This paper
• Outlines a framework for Collaborative Signal
  Processing (CSP) in WSN
• Proposes detection and tracking algorithms
• Implements and validates classification
  algorithms
• Argues that CSP can address challenges with
  classification and tracking
• Suggests CSP algorithms can benefit from
• Distributive processing compute and transmit
  summary statistics
• Goal-oriented, on-demand processing Only perform
  signal processing when a query is present
• Information fusion The farther I am, the fewer
  details I need to know
• Multi-resolution processing Different tasks
  require different rates of sampling in space-time

4
Signal Processing Primitives

• Detection
• Computes running average of signal power over
  some window
• Assumes noise is Gaussian
• Calculates a CFAR threshold based on mean and
  variance
• Event occurs when signal gt CFAR threshold

5
Signal Processing Primitives (2)

• Target Localization
• Assumes isotropic, constant exponent signal
  attenuation model
• Uses energy-based source localization techniques
• Given 4 or more energy readings, uses non-linear
  least squares to find best fit (target location
  that minimizes error)
• Observation Implicitly assumes calibrated and
  localized sensors

6
Tracking of a Single Target

• Assumes a target enters through one of the
  corners
• Active cells A, B, C, D
• Uses energy to detect
• Algorithm
• Nodes in cell detect target and report to manager
• Manager estimates current target location
• Manager predicts future position of target
• Manager creates and initializes new cells
• Manager hands off once the target is detected in
  a new cell

7
Tracking of Multiple Targets

• In the simple case
• Targets occupy distinct space-time cells
• Multiple instances of algorithm can be used in
  parallel
• In general case
• Multiple tracks may cross (simultaneously occupy
  the same space-time cell)
• Data association (which track to associate data
  with?)
• Classification is required to disentangle tracks
• Observation Depending on what the tracks are
  used for, and whether it is permissible to
  discard old state, classification may not be
  required at all.

8
Target Classification

• Focuses on classification at a single node
• Uses acoustic and seismic spectra of wheeled and
  tracked targets as feature vectors
• Extracts feature vectors from time series data
  using FFT
• Elements of the feature vectors are the Fourier
  coefficients (corresponding to the signal power
  at that frequency)
• Acoustic Down-sampled to fs 5kHz, 1000 point
  FFT, only used 0-1kHz BW, then compressed by 4x
  and 10x to obtain 50 and 20 element feature
  vectors
• Seismic fs 256Hz, 256 point FFT using 64
  samples and zero padded data segments

9
Target Classification (2) Acoustic PSD

• Power Spectral Density plots of different targets
  by the same sensor instances
• Note the obvious differences in the prototype
  signatures, allowing clean separations

10
Target Classification (3) Seismic PSD

• Power Spectral Density plots of the same target
  by different sensor instances
• Note the signature differences in 5a and 5c
• What explains these differences?

11
Target Classification (4) Algorithms and
Validation

• Three classification algorithms were tested
• k-Nearest Neighbor
• Maximum Likelihood Classifier
• Support Vector Machine
• Details of the classifiers not discussed here
• To cross-validate the performance of the
  classifiers
• Available data divided into three sets F1, F2,
  F3
• Take two sets at a time for training and one for
  testing
• Experiment A Training F1F2 training Testing
  F3
• Experiment B Training F2F3 training Testing
  F1
• Experiment C Training F1F3 training Testing
  F2

12
Target Classification (5) Acoustic Performance

• SVM demonstrates best performance
• K-NN demonstrates next best performance
• ML demonstrates poorest performance

13
Target Classification (6) Seismic Performance

• SVM demonstrates best performance
• K-NN demonstrates next best performance
• ML demonstrates particularly poor performance for
  Wheeled Targets (77.6 correct classification
  rate)

14
Issues and Challenges

• Collaborative Signal Processing faces many
  real-world hurdles
• Uncertainty in temporal and spatial measurements
• Depends on accuracy of time synchronization
• Depends on accuracy of network node localization
• Variability in experimental conditions
• Classifications assumes that target signatures
  are relatively invariant
• Node locations and orientations may results in
  signature variations
• Environmental factors may alter signals
• These nuisance parameters and be included in a
  higher dimension feature vectors at cost of
  increased processing

15
Issues and Challenges (2) - Doppler Effects

• Perceived frequency is a function of radial
  velocity from source to sensor
• Radial velocity changes as a target passes by
• Observation higher frequencies show greater
  absolute changes in frequency

16
Future Research

• Key directions
• Move toward more collaborative algorithms
• Extend feature space to higher dimensions
• Intra-sensor collaboration modal fusion
• Combine information from multiple sensors in
  single node
• Inter-sensor collaboration centralized
  processing
• Report raw time series data or statistics to a
  central node
• Doppler-based composite hypothesis testing
• Incorporate target velocity, CPA distance, and
  angle between secant and radius (vertex is
  targets position)

17
Conclusions

• Outlined a framework for Collaborative Signal
  Processing in Wireless Sensor Networks
• Proposed detection and tracking algorithms
• Implemented and validated classification
  algorithms
• Discovered that signal or sensor variation can
  cause problems with classification and tracking
• Suggested that CSP can address some of these
  challenges

18
Remarks

• No simulations or empirical evidence supporting
  single or multiple target tracking
• Target models not provided and cell shape and
  creation strategy unclear
• Target tracking algorithm is purely conceptual
• Target tracking is simply the motivating scenario
  for studying classification
• Since multi-target tracking with crossing tracks
  is the motivating scenario, classifier
  performance for superimposed signatures would be
  a good idea
• Only tracking uses CSP
• Max signal does not always occur at CPA
• Interesting mix of position and results paper

19
Discussion