Particle methods for change detection, system identification, and control

Particle methods for change detection, system identification, and control

Particle methods are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. The ability to compute the optimal filter is central to solving important problems in areas such as change detection, parameter estimation,...

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Bibliographic Details
Published in:Proceedings of the IEEE Vol. 92; no. 3; pp. 423 - 438
Main Authors: ANDRIEU, C., DOUCET, A., SINGH, S.S., TADIC, V.B.
Format: Journal Article
Language:English
Published: New York IEEE 01-03-2004
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Change detection
Computer simulation
Control systems
Filtering
Filters
Mathematical models
Nonlinearity
Optimal control
Optimization
Parameter estimation
Sliding mode control
State estimation
Stochastic processes
System identification
Target tracking
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Description
Summary:Particle methods are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. The ability to compute the optimal filter is central to solving important problems in areas such as change detection, parameter estimation, and control. Much recent work has been done in these areas. The objective of this paper is to provide a detailed overview of them.
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ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2003.823142