Multisensor-multitarget sensor management: a unified Bayesian approach

Ronald P. S. Mahler

doi:10.1117/12.488535

25 August 2003 Multisensor-multitarget sensor management: a unified Bayesian approach

Ronald P. S. Mahler

Proceedings Volume 5096, Signal Processing, Sensor Fusion, and Target Recognition XII; (2003) https://doi.org/10.1117/12.488535
Event: AeroSense 2003, 2003, Orlando, Florida, United States

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).”.

Citation Download Citation

Ronald P. S. Mahler "Multisensor-multitarget sensor management: a unified Bayesian approach", Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); https://doi.org/10.1117/12.488535

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