Measurement models for ambiguous evidence using conditional random sets

Ronald P. S. Mahler

doi:10.1117/12.280829

28 July 1997 Measurement models for ambiguous evidence using conditional random sets

Ronald P. S. Mahler

Proceedings Volume 3068, Signal Processing, Sensor Fusion, and Target Recognition VI; (1997) https://doi.org/10.1117/12.280829
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

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); https://doi.org/10.1117/12.280829

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