Particle Methods for Change Detection, System Identification, and Control
- 8 November 2004
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Proceedings of the IEEE
- Vol. 92 (3) , 423-438
- https://doi.org/10.1109/jproc.2003.823142
Abstract
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.Keywords
This publication has 38 references indexed in Scilit:
- Online expectation-maximization type algorithms for parameter estimation in general state space modelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Particle filtering-based fault detection in non-linear stochastic systemsInternational Journal of Systems Science, 2002
- Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systemsIEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 2001
- Approximating and Maximising the Likelihood for a General State-Space ModelPublished by Springer Nature ,2001
- Self-Organizing Time Series ModelPublished by Springer Nature ,2001
- Combined Parameter and State Estimation in Simulation-Based FilteringPublished by Springer Nature ,2001
- A Self-Organizing State-Space ModelJournal of the American Statistical Association, 1998
- Recursive estimation in hidden Markov modelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997
- Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space ModelsJournal of Computational and Graphical Statistics, 1996
- Adaptive Markov Control ProcessesPublished by Springer Nature ,1989