摘要： 针对传统的无损卡尔曼滤波(UKF)算法在对天波超视距雷达进行目标跟踪的过程中存在滤波发散和初始收敛速度慢等问题，提出一种改进的UKF算法。通过引进调节因子对状态矢量和观测矢量的协方差作实时调整，以达到提高滤波结果中状态信息与观测信息的正确率和雷达跟踪系统性能的目的。仿真结果表明，该算法在处理目标跟踪问题时，既可有效抑制UKF算法的发散，又可提高跟踪系统的收敛速度。

关键词: 天波超视距雷达, 无损卡尔曼滤波, 目标跟踪, 径向距离误差, 方位角误差, 调节因子

Abstract: For the slow convergence and divergence problem of the traditional Unscented Kalman Filtering(UKF) algorithm in target tracking, this paper puts forward the improved UKF algorithm. It can real-time adjust the covariance of the state vector and observation vector by introducing adjustment factor, so as to improve the right ratio between the state information and observation information in the filter results and to improve the performance of the tracking system. Simulation results show that the improved UKF algorithm not only can restrain the spread of UKF algorithm, but also can enhance the convergence rate of the tracking system in dealing with target tracking.

Key words: over-the-horizon radar, Unscented Kalman Filtering(UKF), target tracking, radial distance error, azimuth error, adjustment factor

中图分类号: 

• TN955