Rao-Blackwellised particle filtering for fault diagnosis
- 1 January 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 4, 4-1767
- https://doi.org/10.1109/aero.2002.1036890
Abstract
We tackle the fault diagnosis problem using conditionally Gaussian state space models and an efficient Monte Carlo method known as Rao-Blackwellised particle filtering. In this setting, there is one different linear-Gaussian state space model for each possible discrete state of operation. The task of diagnosis is to identify the discrete state of operation using the continuous measurements corrupted by Gaussian noise. The method is applied to the diagnosis of faults in planetary rovers.Keywords
This publication has 13 references indexed in Scilit:
- On-board real-time state and fault identification for roversPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Sequential Monte Carlo Methods in PracticePublished by Springer Nature ,2001
- Sequential Monte Carlo Methods for Optimal FilteringPublished by Springer Nature ,2001
- Sequential Monte Carlo Methods to Train Neural Network ModelsNeural Computation, 2000
- Filtering via Simulation: Auxiliary Particle FiltersJournal of the American Statistical Association, 1999
- Quick simulation: a review of importance sampling techniques in communications systemsIEEE Journal on Selected Areas in Communications, 1997
- Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space ModelsJournal of Computational and Graphical Statistics, 1996
- Contour tracking by stochastic propagation of conditional densityPublished by Springer Nature ,1996
- Novel approach to nonlinear/non-Gaussian Bayesian state estimationIEE Proceedings F Radar and Signal Processing, 1993
- Random sampling approach to state estimation in switching environmentsAutomatica, 1977