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25 May 2005 Unified Bayes filtering with fuzzy and rule-based evidence
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Abstract
This is the first of two conference papers describing a unified approach to the problem of estimating the states of one or more temporally evolving objects, based on fusion of accumulating multi-source information, where such information can take one of two forms: ambiguous measurements or ambiguous state-estimates. It is often asserted that the probabilistic/Bayesian paradigm is too restrictive to successfully address information sources of this kind and that, consequently, alternative paradigms such as fuzzy logic, the Dempster-Shafer theory, or rule-based inference must be used instead. On the other hand, many Bayesians vigorously challenge the very credivility of such approaches. In this paper we show that, as most commonly employed, fusion of fuzzy and rule-based measurements can be subsumed within the Bayesian theory. Our approach is based on natural extensions of the familiar recursive Bayes filter. These extensions rely, in turn, on a sytematic Bayesian analysis used in conjuction with the theory of finite-set statistics (FISST).
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Ronald Mahler "Unified Bayes filtering with fuzzy and rule-based evidence", Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); https://doi.org/10.1117/12.604712
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