Parameter identification of unmanned vehicle MMG model based on improved extended kalman filter

Objectives In order to construct an accurate MMG （Mathematical Model Group）model for a water-jet propulsion unmanned surface vehicle, the traditional extended Kalman filter algorithm and improved extended Kalman filter algorithm are combined with the real boat data for parameter identification. MethodsFirstly, based on the traditional EKF algorithm, in order to make full use of the effective information hidden in the historical data, an improved EKF algorithm combining multi-innovation theory and dynamic forgetting factor is proposed. Then, based on the real unmanned surface vehicle data, the unknown parameters in the MMG model are identified. Finally, the identified parameter values are brought back to the established MMG model, and the rudder angle and main engine speed identical with the real boat data are input. The bow angle, longitudinal speed, transverse speed, bow angular velocity and position information data are obtained through simulation, and the comparative analysis is carried out. Results The results show that compared with the traditional EKF algorithm, the root mean squared error index and the symmetric mean absolute percentage error index of the improved EKF algorithm are closer to 0, in which the root mean squared error index is reduced by 20.02% at the highest, and the symmetric mean absolute percentage error index is reduced by 26.84% at the highest. Conclusions The simulation results show that the improved extended Kalman filter algorithm has higher identification accuracy, which proves the accuracy of the MMG model established by the algorithm.