Fault tolerant estimation of autonomous underwater vehicle dynamics via robust UKF

This article is basically focused on application of the Unscented Kalman Filter (UKF) algorithm to the estimation of high speed an autonomous underwater vehicle (AUV) dynamics. In the normal operation conditions of AUV, conventional UKF gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study, introduces Robust Unscented Kalman Filter (RUKF) algorithms 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's, 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 UKF. Checkout is satisfied via a kind of statistical information. In order to achieve that, the fault detection procedure is introduced. Two different RUKF algorithms, one with single scale factor and one with multiple scale factors, are proposed and applied for the motion dynamics parameters estimation process of an AUV. The results of these algorithms are compared for different types of measurement faults in different estimation scenarios and recommendations about their applications are given.