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Multi-target Sensor Management Using Alpha-Divergence Measures

  • Chris Kreucher
  • Keith Kastella
  • Alfred O. Hero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2634)

Abstract

This paper presents a sensor management scheme based on maximizing the expected Rényi Information Divergence at each sample, applied to the problem of tracking multiple targets. The underlying tracking methodology is a multiple target tracking scheme based on recursive estimation of a Joint Multitarget Probability Density (JMPD), which is implemented using particle filtering methods. This Bayesian method for tracking multiple targets allows nonlinear, non-Gaussian target motion and measurement-to-state coupling. Our implementation of JMPD eliminates the need for a regular grid as required for finite element-based schemes, yielding several computational advantages. The sensor management scheme is predicated on maximizing the expected Rényi Information Divergence between the current JMPD and the JMPD after a measurement has been made. The Rényi Information Divergence, a generalization of the Kullback-Leibler Distance, provides a way to measure the dissimilarity between two densities. We evaluate the expected information gain for each of the possible measurement decisions, and select the measurement that maximizes the expected information gain for each sample.

Keywords

Target Tracking Information Divergence Surveillance Area IEEE Signal Processing Magazine Sensor Management 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Chris Kreucher
    • 1
  • Keith Kastella
    • 1
  • Alfred O. Hero
    • 2
  1. 1.Veridian’s Ann Arbor Research and Development CenterAnn Arbor
  2. 2.Department of Electrical Engineering and Computer ScienceThe University of MichiganAnn Arbor

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