Random-set approach to data fusion

Ronald P. S. Mahler

doi:10.1117/12.181026

29 July 1994 Random-set approach to data fusion

Ronald P. S. Mahler

Proceedings Volume 2234, Automatic Object Recognition IV; (1994) https://doi.org/10.1117/12.181026
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

This paper describes a fundamentally new theoretical approach to data fusion based on a novel type of random variable called the random finite set, and on a generalization of the familiar radon-nikodym derivative from the theory of the Lebesgue integral. We have shown how to directly generalize classical (i.e., single-sensor, single-target) parametric point estimation theory to the multi-sensor, multi-target, localization and classification realm. Using this theory we have shown that it is possible to construct data fusion algorithms in which detection, correlation, tracking and classification are unified into a single probabilistic procedure. We have also shown that a Cramer-Rao inequality holds for a general class of data fusion algorithms, apparently the first ever.

Citation Download Citation

Ronald P. S. Mahler "Random-set approach to data fusion", Proc. SPIE 2234, Automatic Object Recognition IV, (29 July 1994); https://doi.org/10.1117/12.181026

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