机动再入目标的FGPF-BLUE跟踪

FGPF-BLUE Tracking for Maneuvering Reentry Targets

机动再入目标的运动具有明显的非线性,其观测又往往在传感器坐标系下进行,构成强非线性的跟踪问题。为了克服扩展卡尔曼滤波和粒子滤波在跟踪精度和实时性方面的缺点,提出了一种新型的非线性跟踪算法。新型的FGPF-BLUE滤波将快速高斯粒子滤波的预测步骤与最优线性无偏估计的更新步骤相结合,是一种半蒙特卡罗滤波方法。建立了机动再入目标的动态模型,并分别应用扩展卡尔曼滤波、粒子滤波和FGPF-BLUE滤波实现了对该目标的跟踪。通过对各种滤波方法精度和消耗时间的对比,可知新方法的稳态性能优于其他两种算法,实时性优于粒子滤波。

The tracking problems of maneuvering reentry targets often turn out to be highly nonlinear problems, for both of the dynamic and the observation models could be nonlinear. In order to overcome the disadvantages of Extended Kalman Filter （EKF） and Particle Filter （PF） in precision and real-time performance, a new nonlinear filter was proposed. The new FGPF-BLUE （ Fast Gaussian Particle Filter-Best Linear Unbiased Estimator） filter was constructed by combining predictive steps of the FGPF and the updating steps of the BLUE and was, therefore, maneuvering reentry target was established and comparison of those filters shows that the new consumes less time than PF. a ＂ semi-Monte Carlo＂ method. The dynamic model of a was processed by EKF, PF and FGPF-BLUE filter. The filter has a higher steady-state precision than others and consumes less time than PF.