Journal Article

Robust Kalman smoother for EIV state space model based on multivariate least trimmed squares estimator

Jaafar AlMutawa

in IMA Journal of Mathematical Control and Information

Published on behalf of Institute of Mathematics and its Applications

Volume 29, issue 1, pages 23-32
Published in print March 2012 | ISSN: 0265-0754
Published online October 2011 | e-ISSN: 1471-6887 | DOI: http://dx.doi.org/10.1093/imamci/dnr022
Robust Kalman smoother for EIV state space model based on multivariate least trimmed squares estimator

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This paper derives a robust Kalman smoother estimate for the errors-in-variables state space model that is less sensitive to outliers in the sense of the multivariate least trimmed squares (MLTS) method. Since the MLTS estimate is a combinatorial optimization problem, the randomized algorithm has been proposed. However, the uniform sampling method has a high computational cost and may lead to a biased estimate. Therefore, we apply the subsampling method. The algorithm presented here is both efficient and easy to implement. A Monte Carlo simulation result shows the efficiency of the proposed algorithm.

Keywords: errors-in-variables state space model; multivariate least trimmed squares; Kalman filter and smoother; outliers; random search algorithm; subsampling method

Journal Article.  0 words. 

Subjects: Mathematics

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