An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A Bayesian approach to tracking multiple targets using sensorarrays and particle filters
IEEE Transactions on Signal Processing
Distributed particle filters for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Spatio-temporal sampling rates and energy efficiency in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Multi-model motion tracking under multiple team member actuators
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Approximate stochastic dynamic programming for sensor scheduling to track multiple targets
Digital Signal Processing
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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.