无迹卡尔曼滤波器在船舶运动状态估计中的应用

Application of Unscented Kalman Filter in Vessel Motion State Estimation

基于无迹卡尔曼滤波,提出了一种适用于船舶水平运动状态估计的方法。该方法可以根据已知的传感器采样数据(位置、风速、推进器推力等),预测船舶的运动状态,将处于风、浪、流等荷载作用下的船舶的水平运动中的低频运动和波频运动分离,并辨识一些与船舶运动相关的参数,如流速、流向、风速、风向等。通过计算机仿真实验验证了该算法的有效性。

Based on the Unscented Kalman Filter(UKF), a state estimation method for vessel horizontal motion is introduced in this paper. On basis of the known sensor sampling data(location, wind speed, thrust), vessel motion state can be predicted, low-frequency motion and wave-frequency motion can be separated from vessel horizontal motion under wind, wave and current load, as well as some parameters related to vessel motion can be estimated, such as current velocity, current direction, wind velocity, wind direction. At last the effectiveness of this method is determined by computer simulation.