Multi-target Sensor Management Using Alpha-Divergence Measures

This paper presents a sensor management scheme based on maximizing the expected R´enyi 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 elementbased schemes, yielding several computational advantages. The sensor management scheme is predicated on maximizing the expected R´enyi Information Divergence between the current JMPD and the JMPD after a measurement has been made. The R´enyi Information Divergence, a generalization of the Kullback-Leibler Distance, provides a way to measure the dissimilarity between two densiti...

• Type: Conference
• Year: 2003
• Where: IPSN
• Authors: Christopher M. Kreucher, Keith Kastella, Alfred O. Hero III
• Keywords: IPSN 2003, Multiple Targets, R´enyi Information Divergence, Sensor Management Scheme, Sensor Networks