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Proceedings Paper

Multisensor-multitarget sensor management: a unified Bayesian approach
Author(s): Ronald P. S. Mahler
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Paper Abstract

Multisensor-multitarget sensor management is at root a problem in nonlinear control theory. This paper develops a potentially computationally tractable approximation of an earlier (1996) Bayesian control-theoretic foundation for sensor management based on “finite-set statistics” (FISST) and the Bayes recursive filter for the entire multisensor-multitarget system. I analyze possible Bayesian control-theoretic objective functions: Csiszar information-theoretic functionals (which generalize Kullback-Leibler discrimination) and “geometric” functionals. I show that some of these objective functions lead to potentially tractable sensor management algorithms when used in conjunction with MHC (multi-hypothesis correlator)-like algorithms. I also take this opportunity to comment on recent misrepresentations of FISST involving so-called “joint multitarget probabilities (JMP).”.

Paper Details

Date Published: 25 August 2003
PDF: 12 pages
Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); doi: 10.1117/12.488535
Show Author Affiliations
Ronald P. S. Mahler, Lockheed Martin Tactical Systems (United States)


Published in SPIE Proceedings Vol. 5096:
Signal Processing, Sensor Fusion, and Target Recognition XII
Ivan Kadar, Editor(s)

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