非线性滤波算法在无源双基地雷达目标跟踪中的比较研究

Comparison of Nonlinear Filtering for Passive Bistatic Radar Target Tracking

针对无源双基地雷达目标跟踪问题,仿真分析了EKF、UKF、CDF等几种非线性滤波算法的状态估计性能。同时,基于后向平滑估计原理,利用当前观测数据平滑估计前时刻状态变量的均值和方差,提出了一种改进的UKF(CDF)滤波算法-BSUKF/CDF。仿真结果表明,在理想高斯白噪声情况下,UKF/CDF及BSUKF/CDF的跟踪性能相近,但均明显优于EKF;但若考虑角闪烁噪声,BSUKF/CDF的跟踪性能则优于UKF/CDF及EKF。

For passive bistatic radar target tracking problem, the performances of several nonlinear filtering algorithms such as EKF, UKF and CDF were simulated and analyzed. Also, a new nonlinear filtering algorithm called BSUKF/CDF based on backward-smoothing principle was proposed. In BSUKF/CDF algorithm, the current observation was used to smoothly estimate the previous mean and covariance of the state variable. The simulation results show that in Gaussian environment, BSUKF/CDF and UKF/CDF have almost the same tracking performance, and both perform better than EKF; however in angle glint noise environment, BSUKF/CDF perform much better than UKF/CDF and EKF .