Robust Kalman Filter Based Estimation of AUV Dynamics in the Presence Of Sensor Faults

This article is basically focused on application of the Robust Kalman Filter (RKF) algorithm to the estimation of high speed an autonomous underwater vehicle (AUV) dynamics. In the normal operation conditions of AUV, conventional Kalman filter gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, Kalman Filter (KF) gives inaccurate results and diverges by time. This study, introduces Robust Kalman Filter algorithm with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight and the estimations are corrected without affecting the characteristic of the accurate ones. In the presented RUKF, the filter gain correction is performed only in the case of malfunctions in the measurement system and in all other cases procedure is run optimally with regular KF.