Multi-target Sensor Management Using Alpha-Divergence Measures
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.
KeywordsTarget Tracking Information Divergence Surveillance Area IEEE Signal Processing Magazine Sensor Management
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- 1.K. Kastella, “Joint multitarget probabilities for detection and tracking”, SPIE Proceedings, Acquisition, Tracking and Pointing XI, 21–25 April, 1997, Orlando, FL.Google Scholar
- 2.S.S. Blackman, Mulitple-Target Tracking with Radar Applications. Norwood, MA. Artech House, 1986.Google Scholar
- 3.Alfred O. Hero III, Bing Ma, Olivier J.J. Michel, and John Gorman, “Applications of Entropic Spanning Graphs”, IEEE Signal Processing Magazine, September 2002, pp. 85–95.Google Scholar
- 4.Arulampalam, M., Maskell, S., Gordon, N. and Clapp, T. “A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking”, IEEE Transactions on Signal Processing, February 2002.Google Scholar
- 6.Liu, J. and Chen, R. “Sequential Monte Carlo Methods for Dynamic Systems”, Journal of the American Statistical Association, September 1998.Google Scholar
- 8.A. Rényi, “On measures of entropy and information”, Proc. 4th Berkeley Symp. Math. Stat. and Prob., volume 1, pp. 547–561, 1961.Google Scholar
- 9.D. Sinno and D. Kreithen, “A Constrained Joint Optimization Approach to Dynamic Sensor Configuration”, Thirty Six Asilomar Conference on Signals, Systems, and Computers, November 2002.Google Scholar
- 11.F. Zhao, J. Shin, and J. Reich, “Information-Driven Dynamic Sensor Collaboration”, IEEE Signal Processing Magazine, March 2002, pp. 61–72.Google Scholar