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的问题,提出了一种修正极坐标系下的自适应卡尔曼滤波算法,对虚拟系统噪声进行估计,动态补偿模型线性化误差,对其滤波理论及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,优于一般的扩展卡尔曼滤波算法。
A modified polar adaptive extended Kalman filter(MPAEKF)algorithm is presented in order to solve the problem of instability and low accuracy of the passive filter in bearings-only target tracking. This algorithm can be used to estimate the statistics of the state virtual noise on-line, thus it overcomes the bad effect caused by the linearization of nonlinear state model. The simulation result shows that MPAEKF improves the filter convergence rate and accuracy.
出处
《海军工程大学学报》
CAS
2004年第3期69-73,共5页
Journal of Naval University of Engineering
关键词
纯方位
目标跟踪
修正极坐标
自适应滤波
bearings-only
target tracking
modified polar coordinate
adaptive filtering
