Adaptive unscented kalman filter for deep-sea tracked vehicle localization

Abstract-This paper introduces a kinematic model of a deep-sea mining vehicle in presence of sliding parameters. The model describes both the noises features of sliding parameters and the deep-sea condition features. To handle sliding parameters noises, a recursive algorithm to minimize difference between the filter-computed and the actual innovation covariance is adopted, which is a novel integrated navigation method based on unscented Kalman filters (UKF). Taking into account the influence of measurement data delay, UKF fuses the localization information of long base line (LBL) sonar localization system and dead-reckoning (DR) to perform the state estimation. Simulation results show that the adaptive UKF has better localization estimation than a normal UKF for a deep-sea tracked vehicle (DTV).