Back to Search
Start Over
Robust Kalman filter and smoother for errors-in-variables state space models with observation outliers based on the minimum-covariance determinant estimator.
- Source :
- Asian Journal of Control; Jul2011, Vol. 13 Issue 4, p513-521, 9p, 5 Graphs
- Publication Year :
- 2011
-
Abstract
- In this paper, we propose a robust Kalman filter and smoother for the errors-in-variables (EIV) state space models subject to observation noise with outliers. We introduce the EIV problem with outliers and then present the minimum covariance determinant (MCD) estimator which is a highly robust estimator in terms of protecting the estimate from the outliers. Then, we propose the randomized algorithm to find the MCD estimate. However, the uniform sampling method has a high computational cost and may lead to biased estimates, therefore we apply the sub-sampling method. A Monte Carlo simulation result shows the efficiency of the proposed algorithm. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [ABSTRACT FROM AUTHOR]
- Subjects :
- KALMAN filtering
ANALYSIS of covariance
MONTE Carlo method
ALGORITHMS
RANDOM sets
Subjects
Details
- Language :
- English
- ISSN :
- 15618625
- Volume :
- 13
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Asian Journal of Control
- Publication Type :
- Academic Journal
- Accession number :
- 61352257
- Full Text :
- https://doi.org/10.1002/asjc.352