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 ,而导致滤波精度很差 ,该文提出了一种直角坐标系下的自适应卡尔曼滤波算法 ,对虚拟噪声进行估计 ,动态补偿观测模型线性化误差 ,消减系统的观测误差 ,并对其滤波理论及其算法进行了研究和仿真。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 ,优于一般的扩展卡卡尔曼滤波算法 。
Concerning the problem of instability and low accuracy of the passive filter in bearings-only target tracking, a modified polar coordinate adaptive extended Kalman filter(MPAEKF) algorithm is presented. Owing to estimating the statistics of the state virtual noise on-line, it overcomes the bad effect caused by linearization of nonlinear state model. The simulation result shows that the MPAEKF improves the filter convergence rate and the accuracy.
出处
《计算机仿真》
CSCD
2004年第6期78-82,共5页
Computer Simulation
