直角坐标系下的水下被动目标跟踪自适应卡尔曼滤波算法

An adaptive Kalman filter algorithm used in the underwater passive target tracking in a Cartesian coordinate system

摘要: 针对纯方位被动目标跟踪中,直角坐标系下的扩展卡尔曼滤波器容易发散,导致滤波精度很差的情况,文章中提出了一种直角坐标系下自适应卡尔曼滤波算法,对虚拟噪声进行了估计,动态补偿观测模型的线性化误差,削减系统的观测误差,并对其滤波理论及其算法进行了研究和仿真,结果表明,该算法提高了滤波的稳定性、快速性和精确性,优于一般的扩展卡尔曼滤波算法。

Abstract: Taking into account the instability and low accuracy of passive filters in bearings-only target tracking, a modified polar coordinate adaptive extended Kalman filter (MPAEKF) algorithm is presented. Virtual noise is estimated, and errors due to linearization are dynamically compensated for so that the system’s observation error is reduced. The filtering theory and the algorithm are studied. Simulation results show that MPAEKF can improve the filter convergence and accuracy.