Multiple-model estimation with variable structure: model-group switching algorithm

A general multiple-model estimator with variable structure (VSMM), called model-group switching algorithm, is presented. It assumes that the total set of models can be covered by a number of model groups, each representing a cluster of closely related system behavior patterns or structures, and a pa...

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Bibliographic Details
Published inProceedings of the 36th IEEE Conference on Decision and Control Vol. 4; pp. 3114 - 3119 vol.4
Main Authors Li, X.P., Youmin Zhang, Xiaorong Zhi
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Algorithm design and analysis
Clustering algorithms
History
Noise measurement
State estimation
Stochastic systems
Target tracking
Time measurement
Uncertainty
Vectors
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ISBN0780341872
9780780341876
ISSN0191-2216
DOI10.1109/CDC.1997.652320

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Summary:A general multiple-model estimator with variable structure (VSMM), called model-group switching algorithm, is presented. It assumes that the total set of models can be covered by a number of model groups, each representing a cluster of closely related system behavior patterns or structures, and a particular group is running at any given time determined by a hard decision. This algorithm is the first VSMM estimator that is generally applicable to a large class of problem with hybrid (continuous and discrete) uncertainties and easily implementable. The algorithm is promising in the sense of being substantially more cost-effective than the interacting multiple-model estimator.
ISBN:0780341872
9780780341876
ISSN:0191-2216
DOI:10.1109/CDC.1997.652320