On-line state and parameter estimation of an under-actuated underwater vehicle using a modified Dual Unscented Kalman Filter

This paper presents a novel modification of the Dual Unscented Kalman Filter (DUKF) for the on-line concurrent state and parameter estimation. The developed algorithm is successfully applied to an under-actuated underwater vehicle. Like in the case of conventional DUKF the proposed algorithm demonstrates quick convergence of the parameter vector. In addition, experimental results indicate an increased performance when the proposed methodology is utilized. The applicability and performance of the proposed algorithm is experimentally verified by combining the proposed DUKF with a non-linear controller on a modified Videoray ROV in a test tank. The on-line estimation of the vehicle states and dynamic parameters is achieved by fusing data from a Laser Vision System (LVS) and an Inertial Measurement Unit (IMU).