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Proceedings Paper

Unified robust-Bayes multisource ambiguous data rule fusion
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Paper Abstract

The ambiguousness of human information sources and of a PRIORI human context would seem to automatically preclude the feasibility of a Bayesian approach to information fusion. We show that this is not necessarily the case, and that one can model the ambiguities associated with defining a "state" or "states of interest" of an entity. We show likewise that we can model information such as natural-language statements, and hedge against the uncertainties associated with the modeling process. Likewise a likelihood can be created that hedges against the inherent uncertainties in information generation and collection including the uncertainties created by the passage of time between information collections. As with the processing of conventional sensor information, we use the Bayes filter to produce posterior distributions from which we could extract estimates not only of the states, but also estimates of the reliability of those state-estimates. Results of testing this novel Bayes-filter information-fusion approach against simulated data are presented.

Paper Details

Date Published: 25 May 2005
PDF: 11 pages
Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); doi: 10.1117/12.605466
Show Author Affiliations
A. El-Fallah, Scientific Systems Co., Inc. (United States)
A. Zatezalo, Scientific Systems Co., Inc. (United States)
R. Mahler, Lockheed Martin Tactical Defense Systems (United States)
R. K. Mehra, Scientific Systems Co., Inc. (United States)

Published in SPIE Proceedings Vol. 5809:
Signal Processing, Sensor Fusion, and Target Recognition XIV
Ivan Kadar, Editor(s)

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