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28 July 1997 Measurement models for ambiguous evidence using conditional random sets
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In several recent papers we have shown how random set theory provides a theoretically rigorous foundation for much of data fusion. An important missing piece in our approach has been the problem of how to incorporate observations which are ambiguous (e.g. imprecise, fuzzy/vague, contingent, etc.) into conventional Bayesian estimation and filtering theory. If one can do this, the fusion of imprecise observations with ambiguous observations, generated by dynamic (i.e., moving) targets, becomes possible using a familiar Bayes-Markov nonlinear filtering approach. This paper sketches the basis for fusion if one assumes that both observation space and state space are finite.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ronald P. S. Mahler "Measurement models for ambiguous evidence using conditional random sets", Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997);

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