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Proceedings Paper

Formal versus heuristic modeling for multitarget Bayes filtering
Author(s): Ronald P. S. Mahler
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Paper Abstract

The multisensor-multitarget Bayes filter is the foundation for multi-sensor-multitarget detection, tracking, and identification. This paper addresses the question of principled implementation of this filter. Algorithms can always be cobbled together using catch-as-catch-can heuristic techniques. In formal Bayes modeling one instead derives statistically precise, implementation-independent equations from which principle approximations can then be derived. Indeed, this has become the accepted methodology for single-sensor, single-target tracking R&D. In the case of the multitarget filter, however, partisans of a so-called "plain-vanilla Bayesian approach" have disparaged formal Bayes modelling, and have protrayed specific, ad hoc implementations as completely general, "powerful and robust computational methods." In this and a companion paper I expose the speciousness of such claims. This paper reviews the elements of formal Bayes modeling and approximation, describes what they must look like in the multitarget case, and contrasts them with the "plain-vanilla Bayesian approach."

Paper Details

Date Published: 25 August 2004
PDF: 12 pages
Proc. SPIE 5428, Signal and Data Processing of Small Targets 2004, (25 August 2004); doi: 10.1117/12.544509
Show Author Affiliations
Ronald P. S. Mahler, Lockheed Martin Tactical Systems (United States)


Published in SPIE Proceedings Vol. 5428:
Signal and Data Processing of Small Targets 2004
Oliver E. Drummond, Editor(s)

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