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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。
Aiming at the target tracking problem of passive bistatic radar, state estimation performance of several nonlinear filtering algorithms such as EKF, UKF and CDF is simulated and analyzed. At the same time, based on the principle of backward smoothing estimation, an improved UKF (CDF) filtering algorithm -BSUKF / CDF is proposed by using the mean and variance of the state variables before the current observation data are smoothed. The simulation results show that the tracking performance of UKF / CDF and BSUKF / CDF is similar to that of EKF in the case of ideal Gaussian white noise, but the tracking performance of BSUKF / CDF is better than that of UKF / CDF And EKF.