Particle methods for change detection, system identification, and control

被引：197
作者：
Andrieu, C [1 ]
Doucet, A
Singh, SS
Tadic, VB
机构：
[1] Univ Bristol, Dept Math, Bristol BS8 1TW, Avon, England
[2] Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England
[3] Univ Melbourne, Dept Elect Engn, Melbourne, Vic 3010, Australia
[4] Univ Sheffield, Dept Automat Control, Sheffield S1 3JD, S Yorkshire, England
来源：
PROCEEDINGS OF THE IEEE | 2004年 / 92卷 / 03期
关键词：
change detection; control; optimal filtering; parameter estimation; sequential Monte Carlo; state-space models; stochastic approximation;
D O I：
10.1109/JPROC.2003.823142
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：423 / 438
页数：16
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