Parametric Identification of Ship Abkowitz Maneuvering Model Using Extend Kalman Filter

Ship motion modeling plays an important role in improving the control system of ships and improving the maneu-verability of ships. The main work of this paper is identification modeling, which predicts ship maneuvering by identifying the corresponding hydrodynamic coefficients. First, the observation data is processed using Extend Kalman Filter (EKF). The preprocessing of the data can effectively reduce the influence of observation noise on the identification results. The Extend Kalman Filter algorithm is used to identify the parameters of the processed observation data, and the difference between the identification results and the true value is compared. Finally, the identification results are used to simulate the ship maneuvering motion, the ship trajectory tracking and rudder angle response are well matched.