基于PLKF的固定单站无源定位与跟踪算法研究

利用到达方向（DOA）和多普勒频率（DF）建立了固定单站对空中运动辐射源的无源定位与跟踪模型,推导了该模型下的伪线性测量方程,用伪线性卡尔曼滤波（PLKF）算法实现了定位与跟踪;在此基础上用k时刻的状态估计值代替一步预测值对该算法进行了改进;最后与扩展卡尔曼滤波（EKF）算法进行比较。仿真结果表明,改进的PLKF算法具有更快的收敛速度和更高的收敛精度,PLKF算法克服了EKF算法的一些缺点。

Research on the Non-Moving Single Observer Passive Location and Tracking Based on PLKF Algorithm

Two-dimension location and tracking model was established using DOA and DF information, and the Pseudo-linear measurement equation was derived from the model. Based on this, PLKF algorithm was used to locate and track the moving target. Then the state estimation at time k was used to substitute for the first step principium forecast to improve the PLKF algorithm. At last. a comparison was made with the EKF algorithm The computer simulation indicates that the improved PLKF algorithm has higher precision and faster convergence. PLKF algorithm has avoided some deficiency in EKF algorithm