A robust unscented Kalman filter and its application in estimating dynamic positioning ship motion states

Estimating dynamic positioning ship motion states is complex if the measured nonlinear motion data have outlying data caused by faulty sensors or ocean environmental noises. To overcome the adverse effects of sensor outliers, we developed a modified unscented Kalman filter (MUKF) algorithm. An outlier detection function was first established to spot the outliers in the measurement and then embedded into the regular unscented Kalman filter (UKF) algorithm to modify the covariance of measurement noise for obtaining smooth changes of the filter gain. To verify the developed MUKF algorithm, two dynamic positioning ship motions were simulated for estimating ship motion states in the presence of measurement outliers. Four outlier scenarios with different extents of sensor faults, different points at which the outlier occurred, and outlier duration during the ship motion course were simulated. The estimated values were compared with the theoretical ones. Additional parameter sensitivity was then performed to verify the stability and convergence performance of the developed MUKF algorithm. The results estimated by the robust MUKF were accurate and reliable, regardless of the outlier scenario, indicating the robustness of the MUKF algorithm to reduce the influence of outliers on the estimation of dynamic positioning ship motion states. The implications of this study are also discussed and presented.