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Proceedings Paper

A unified Bayesian theory of measurements
Author(s): Ronald Maher
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Paper Abstract

Bayesian target detection, tracking, and identification is based on the recursive Bayes filter and its generalizations. This filter requires that measurements be transformed into likelihood values. Conventional likelihoods model the randomness of conventional measurements. Other measurement types involve not only randomness but also imprecision, vagueness, uncertainty, and contingency. Conventional measurements and target states are also mediated by precise, deterministic models. But in general these models can also involve imprecision, vagueness, or uncertainty. This paper describes three major types of generalized measurements and their associated generalized likelihood functions. If measurements are "UGA measurements" then fuzzy, Dempster-Shafer, and rule-based measurement fusion can be rigorously reformulated as special cases of Bayes' rule.

Paper Details

Date Published: 7 May 2007
PDF: 12 pages
Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 65670P (7 May 2007); doi: 10.1117/12.721126
Show Author Affiliations
Ronald Maher, Lockheed Martin MS2 Tactical Systems (United States)

Published in SPIE Proceedings Vol. 6567:
Signal Processing, Sensor Fusion, and Target Recognition XVI
Ivan Kadar, Editor(s)

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