Model-set design for multiple-model method. Part i

X. Rong Li

doi:10.1109/ICIF.2002.1021127

Model-set design for multiple-model method. Part i

X. Rong Li

University of New Orleans

Research output: Contribution to conference › Paper › peer-review

16 Scopus citations

Abstract

The most important problem in the application of the multiple-model approach to estimation is the design of the model set. This paper deals with this challenging topic in a general setting. Modeling of models as well as true mode as random variables is proposed. Several general methods for design of model sets, along with the initial model probabilities, are presented. They include distribution approximation, minimizing mismatch between mode and models, and moment matching. Examples that demonstrate how the general results presented here can be applied are presented in Part II

Original language	English
Pages	26-33
Number of pages	8
DOIs	https://doi.org/10.1109/ICIF.2002.1021127
State	Published - 2002
Externally published	Yes
Event	5th International Conference on Information Fusion, FUSION 2002 - Annapolis, MD, United States Duration: 8 Jul 2002 → 11 Jul 2002

Conference

Conference	5th International Conference on Information Fusion, FUSION 2002
Country/Territory	United States
City	Annapolis, MD
Period	8/07/02 → 11/07/02

Keywords

Multiple models
adaptive estimation
model-set design
target tracking
variable structure

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10.1109/ICIF.2002.1021127

Cite this

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abstract = "The most important problem in the application of the multiple-model approach to estimation is the design of the model set. This paper deals with this challenging topic in a general setting. Modeling of models as well as true mode as random variables is proposed. Several general methods for design of model sets, along with the initial model probabilities, are presented. They include distribution approximation, minimizing mismatch between mode and models, and moment matching. Examples that demonstrate how the general results presented here can be applied are presented in Part II",

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KW - Multiple models

KW - adaptive estimation

KW - model-set design

KW - target tracking

KW - variable structure

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Model-set design for multiple-model method. Part i

Abstract

Conference

Keywords

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