摘要
针对被动跟踪中常见的滤波发散、收敛速度慢和跟踪精度低等问题,研究了一种非线性系统的自适应推广卡尔曼滤波算法。该算法能够在线估计噪声的统计特性,动态补偿模型线性化误差,消减系统的观测误差。对其滤波理论及算法进行了研究与仿真,结果证实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。
In this paper, we present an adaptive extended Kalman filter（AEKF） algorithm aiming at the issues such as divergence, slow convergence and low precision of filters in passive targets tracking. The algorithm can estimate the statistics features of the virtual state noise on- line and compensate the error caused by model＇ s non-linearization and reduce the error in observation for systems. Simulation results show that the algorithm improves the filtering convergence rate and the accuracy, and is better than the original extended Kalman filter（ EKF）.
出处
《无线电工程》
2007年第5期30-32,共3页
Radio Engineering
