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.
|Translated title of the contribution||Particle methods for change detection, system identification, and control|
|Pages (from-to)||423 - 438|
|Number of pages||16|
|Journal||Proceedings of the IEEE|
|Publication status||Published - Mar 2004|
Bibliographical notePublisher: IEEE-Inst Electrical Electronics Engineers Inc
Other identifier: IDS number 800CA