Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 23-32 |
| Number of pages | 10 |
| Journal | IMA Journal of Mathematical Control and Information |
| Volume | 29 |
| Issue number | 1 |
| DOIs | |
| State | Published - Mar 2012 |
Bibliographical note
Funding Information:The research of the author was partially supported by SABIC FAST TRUCK under grant FT070005.
Keywords
- Kalman filter and smoother
- errors-in-variables state space model
- multivariate least trimmed squares
- outliers
- random search algorithm
- subsampling method
ASJC Scopus subject areas
- Control and Systems Engineering
- Control and Optimization
- Applied Mathematics