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Proceedings Paper

Model-set design, choice, and comparison for multiple-model estimation
Author(s): X. Rong Li; Chen He
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Paper Abstract

This paper deals with the design, choice, and comparison of model sets in the multiple-model (MM) approach to adaptive estimation. Most representative problems of model-set choice and design are considered. As the basis of model-set choice and design, criteria for model-set comparison and choice based on base-state estimation, mode estimation, mode identification, hybrid-state estimation, and hypothesis testing are presented first. Several computationally efficient and easily implementable solutions of the model- set choice problems based on sequential hypothesis tests are presented. Some of these solutions are optimal. Their effectiveness is verified via simulation. How these criteria and result can be used for model-set design is demonstrated via several examples. It is also demonstrated how a probabilistic model of possible scenarios can be constructed.

Paper Details

Date Published: 4 October 1999
PDF: 13 pages
Proc. SPIE 3809, Signal and Data Processing of Small Targets 1999, (4 October 1999); doi: 10.1117/12.364047
Show Author Affiliations
X. Rong Li, Univ. of New Orleans (United States)
Chen He, Univ. of New Orleans (United States)

Published in SPIE Proceedings Vol. 3809:
Signal and Data Processing of Small Targets 1999
Oliver E. Drummond, Editor(s)

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