A robust Kalman filter scheme is proposed to resist the influence of the outliers in the observations. Two kinds of observation error are studied, i.e., the outliers in the actual observations and the heavy-tailed distribution of the observation noise. Either of the two kinds of errors can seriously degrade the performance of the standard Kalman filter. In the proposed method, a judging index is defined as the square of the Mahalanobis distance from the observation to its prediction.
Chang, G. Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion,
Springer Berlin Heidelberg, 2014, с. 391-401.
Chang, G. .
Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion.
: Springer Berlin Heidelberg, 2014, с. 391-401.
Chang, G. (2014)
Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion,
: Springer Berlin Heidelberg, с. 391-401
Chang, G.
(2014).
Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion. Journal of Geodesy. Springer Berlin Heidelberg 88 (4), с. 391-401.
