基于自适应UKF的单站无源定位算法

Single-Observer Passive Location Algorithm Based on Adaptive UKF

针对无迹卡尔曼滤波(UKF)算法在单站无源定位中滤波的性能容易受到初始值和系统噪声影响的问题,提出了一种自适应无迹卡尔曼滤波(AUKF)的跟踪算法。该算法利用观测信息和新息,引入自适应因子,对在滤波过程中的误差的协方差矩阵进行合理自适应调整,保证得到较稳定和高精度的滤波值,从而提高算法的鲁棒性。仿真结果表明,该AUKF算法与扩展卡尔曼滤波算法(EKF)及其衍生算法中的修正协方差滤波算法(MVEKF)和UKF算法相比,对系统噪声的鲁棒性更好,体现在滤波的收敛速度和滤波精度等方面都有所提高,是一种性能更加优越的算法。

Since the unscented Kalman filter algorithm in single observer passive localization is sensi- tive to initial values and system noises, an improved adaptive unscented Kalman filter algorithm is presented. To improve the robustness of the unscented Kalman filter algorithm, an adaptive factor is introduced based on the observation information and new information to adaptively adjust error covar- lance matrix. Simulation results show that compared with the extended Kalman filter （EKF） algo- rithm, the modified covariance extend Kalman filter （MVEKF） algorithm, and the UKF, this adap- tive unscented Kalman filter algorithm can contribute to better filter convergence and higher precision with enhanced robustness.