弹道式再入目标跟踪方法对比分析

地基雷达对弹道式再入目标进行滤波跟踪时主要存在两个导致滤波误差增大的不确定因素，一是弹道系数未知，二是不可准确确定过程噪声协方差矩阵。为此，采用交互式多模型无敏滤波（iterative multiple model unscented filter, IMM UF）算法对弹道式再入目标进行跟踪，选取不同的弹道系数初值和过程噪声协方差矩阵构成合适的模型集合进行了仿真分析，并将其滤波结果与扩展卡尔曼滤波（extended Kalman filter, EKF）和无敏滤波（unscented filter, UF）的滤波结果进行了对比分析，同时还分析比较了IMM UF和自治多模型（autonomous multiple model, AMM）UF算法的跟踪滤波性能。从仿真结果可以看出,采用的IMM UF算法和相应的模型集合可以在先验信息缺少的情况实现对弹道式再入目标更高精度的跟踪。

Comparative study on methods for tracking ballistic reentry targets

Two important uncertain factors for tracking ballistic reentry targets by a ground based radar will result in big filtering bias. One is that the ballistic coefficient is usually unknown; the other is the uncertain process noise matrix. In order to resolve the problem, the paper adopts an iterative multiple model unscented filter (IMM UF) to track ballistic reentry targets, and chooses the models with different ballistic coefficients and different process noise matrices as the components of the model bank. The paper compares the simulation results of IMM UF with those of extended Kalman filter (EKF) and UF, and then compares the filtering performances of IMM UF and autonomous multiple model (AMM) UF. The simulation results prove that the proposed method can track the ballistic reentry targets without prior information more precisely.