|Adaptive Unscented Kalman Filter Based Estimation and Filtering for Dynamic Positioning with Model Uncertainties
Fang Deng*, Hua-Lin Yang*, and Long-JinWang
International Journal of Control, Automation, and Systems, vol. 17, no. 3, pp.667-678, 2019
Abstract : "A novel adaptive unscented Kalman filter (AUKF) is presented and applied to ship dynamic positioning
(DP) system with model uncertainties of time-varying noise statistics, model mismatch and slow varying drift
forces. The adaptive algorithm is proposed to simultaneously online adapt the process and measurement noise
covariance by adopting the main principle of covariance matching. The measurement noise covariance is adapted
based on residual covariance matching method, and then the process noise covariance is adjusted by using adaptive
scaling factor. Simulation comparisons among the proposed RQAUKF, the strong tracking UKF (RSTAUKF) and
the standard UKF show that the proposed RQAUKF can effectively improve the estimation accuracy and stability,
and can assist the controller to obtain better control performance."
"Adaptive unscented Kalman filter, dynamic positioning, estimation and filtering, residual covariance matching, strong tracking."