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Published December 2009 | Published
Book Section - Chapter Open

Multiple target detection using Bayesian learning


n this paper, we study multiple target detection using Bayesian learning. The main aim of the paper is to present a computationally efficient way to compute the belief map update exactly and efficiently using results from the theory of symmetric polynomials. In order to illustrate the idea, we consider a simple search scenario with multiple search agents and an unknown but fixed number of stationary targets in a given region that is divided into cells. To estimate the number of targets, a belief map for number of targets is also propagated. The belief map is updated using Bayes' theorem and an optimal reassignment of vehicles based on the values of the current belief map is adopted. Exact computation of the belief map update is combinatorial in nature and often an approximation is needed. We show that the Bayesian update can be exactly computed in an efficient manner using Newton's identities and the detection history in each cell.

Additional Information

© 2009 IEEE. The authors gratefully acknowledge DARPA for funding this research. This work was supported in part by DARPA DSO under AFOSR contract FA9550-07-C-0024. The authors also thank Ozgur Erdinc for bringing [23] to their attention.

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