基于MPC-EKF的方位角及其变化率联合无源定位算法

Passive Tracking Algorithm with Bearings and Their Changing Rate Measurement Based on MPC-EKF

考虑到单站测向无源定位具有定位速度慢、定位精度较低等缺点,这里在修正极坐标系(MPC)下利用扩展卡尔曼滤波(EKF)算法对基于方位角及其变化率信息的单站无源定位与跟踪问题进行了研究,并在同一仿真环境下对利用方位角及其变化率信息和只利用方位角信息的目标跟踪结果进行了比较分析。由仿真结果可看出,在修正的极坐标系下利用方位角及其变化率信息具有更快的收敛速度和更高的跟踪精度。

Since the single observer passive tracking algorithm using bearing measurement only has some disadvantage such as slow location speed, low location accuracy. The single observer passive tracking problem with bearings and their changing rate measurements was studied, and the extended Kalman filter （EKF） in the modified polar coordinate （MPC） was adopted to track the target. The tracking results of using bearing measurements only and using bearings and their changing rate measurements were compared in the same simulation condition. The simulation results show that the location speed and location accuracy all can be improved through using bearings and their changing rate measurements to track the target in the modified polar coordinate.