Kalman filtering algorithm in presence of outliers

Abstract

A Kalman Filtering algorithm which is robust to observational outliers is developed by assuming that the measurement error may come from either one of two normal distributions and that transition between these distribution is governed by a Markov Chain. The state estimate is obtained as a weighted average of the estimates from the two parallel filters where the weights are the posterior probabilities. The impotents obtained by this Robust Kalman Filter in the presence of outliers is demonstrated with examples.