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Proceedings Paper

Model-set sequence-conditioned estimation for variable-structure MM estimation
Author(s): X. Rong Li
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Paper Abstract

The most important, natural and practical approach to variable-structure multiple-model estimation is the recursive adaptive model-set (RAMS) approach. It consists of two functional components: model-set adaptation and model- set sequence conditioned estimation. This paper makes contribution to the second component. Specifically, a general, optimal single-step and highly efficient recursion for model-set sequence conditioned estimation based on an arbitrary time-varying model-set sequence is obtained by an extension of the well-known interacting multiple-model (IMM) algorithm. This recursion provides a natural and systematic algorithm, which is optimal within the RAMS approach, for assigning the probabilities to newly activated models and initializing the filters based on these models. In addition, an optimal and highly efficient fusion method is presented for obtaining the overall estimate from these based on two arbitrary model sets, not necessarily disjoint. The optimal recursion and fusion provide a solution to the problem of model-set sequence conditioned estimation that is fairly satisfactory for most practical situations. The results presented here have been employed in the recent development of two variable-structure MM estimators, the likely-mode set and the model-group switching algorithms, that are generally applicable, easily implementable, and significantly superior the best fixed-structure MM estimators available.

Paper Details

Date Published: 3 September 1998
PDF: 13 pages
Proc. SPIE 3373, Signal and Data Processing of Small Targets 1998, (3 September 1998); doi: 10.1117/12.324650
Show Author Affiliations
X. Rong Li, Univ. of New Orleans (United States)


Published in SPIE Proceedings Vol. 3373:
Signal and Data Processing of Small Targets 1998
Oliver E. Drummond, Editor(s)

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