28 July 1997 Measurement models for ambiguous evidence using conditional random sets
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Abstract
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.
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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); doi: 10.1117/12.280829; https://doi.org/10.1117/12.280829
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