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Proceedings Paper

Design and evaluation of a model-group switching algorithm for multiple-model estimation with variable structure
Author(s): X. Rong Li; Youmin Zhang; Xiaorong Zhi
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Paper Abstract

A variable-structure multiple-model (VSMM) estimator, called model- group switching (MGS) algorithm, has been developed recently. It is the first VSMM estimator that is generally applicable to a large class of problem with hybrid (continuous and discrete) uncertainties. In this algorithm, the model set is made adaptive by switching among a number of predetermined groups of models. It has the potential to be substantially superior to fixed-structure MM estimators, including the interacting multiple-model (IMM) estimator. Many issues in the application of this algorithm are investigated, such as the model-group activation logic and model- group design, via a detailed design for a problem of tracking a maneuvering target using a time-varying set of models, each characterized by a representative value of the target's expected acceleration. Simulation results are given to demonstrate the performance (based on reasonable and complete measures) and computational complexity of the MGS algorithm, relative to the IMM estimators, under carefully designed random and deterministic scenarios.

Paper Details

Date Published: 29 October 1997
PDF: 12 pages
Proc. SPIE 3163, Signal and Data Processing of Small Targets 1997, (29 October 1997); doi: 10.1117/12.283966
Show Author Affiliations
X. Rong Li, Univ. of New Orleans (United States)
Youmin Zhang, Univ. of New Orleans (United States)
Xiaorong Zhi, Univ. of New Orleans (United States)


Published in SPIE Proceedings Vol. 3163:
Signal and Data Processing of Small Targets 1997
Oliver E. Drummond, Editor(s)

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