ROBUST KALMAN FILTER AND SMOOTHER FOR ERRORS-IN-VARIABLES STATE SPACE MODELS WITH OBSERVATION OUTLIERS BASED ON THE MINIMUM-COVARIANCE DETERMINANT ESTIMATOR

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.