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- W2016134490 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." @default.
- W2016134490 created "2016-06-24" @default.
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- W2016134490 date "1997-07-28" @default.
- W2016134490 modified "2023-09-23" @default.
- W2016134490 title "Measurement models for ambiguous evidence using conditional random sets" @default.
- W2016134490 doi "https://doi.org/10.1117/12.280829" @default.
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