Robust Kalman filter and smoother for errors-in-variables state space models with observation outliers based on the minimum-covariance determinant estimator

• Published in: Asian journal of control Vol. 13; no. 4; pp. 513 - 521
• Main Author: Almutawa, Jaafar
• Format: Journal Article
• Language: English
• Published: BP, Asia (Melbourne) John Wiley and Sons Asia Pte Ltd 01.07.2011; Wiley Subscription Services, Inc
• Subjects: Errors-in-variables model; Kalman filter and smoother; minimum covariance determinant; outliers; random search algorithm; state space models; sub-sampling method
• ISSN: 1561-8625; 1934-6093
• DOI: 10.1002/asjc.352

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