Abstract: The maneuverability prediction accuracy of high-speed unmanned surface vessel（USV）depends on the accuracy of parameter acquisition in its motion model. The high precision model parameters are difficultly obtained by commonly using the extended Kalman filter（EKF）method. The maximum likelihood（ML）method is used to identify the second-order nonlinear response model parameters of unmanned maneuvering motion. 20° zigzag simulation experiment is carried out to collect the data of heading angle and rudder angle with the parameters of an USV response model. A ML identification method is designed based on identification principle and the forward difference method, and the model parameters are obtained by identification. Further research finds that part of parameters identified by ML method are inaccurate because of parameter drift. The analysis shows that the reason of parameter drift is to neglect the influence of the rudder angle change rate for processing the zigzag experimental data by the difference method. An improved identification research based on ML method with sine simulation experimental data was carried out, in which the rudder angle change rate can be directly derived from the rudder angle. The simulation experiments of USV maneuverability motion based on the results identified by ML and EKF method were carried out. The experimental results show that the result identified by ML method is more accurate than that identified by EKF method, and the parameter drift can be solved effectively by identifying with sine simulation experimental data to improve the identification accuracy of ML method. Key

Key words: unmannedsurfacevessel, maneuveringresponsemodel, parameteridentification, maximumlikelihoodmethod, sinesimulation