基于概率假设密度滤波和动力学方程约束的空间群目标数量和位置分辨

空间目标具有射程远、速度快等特点，为了有效解决密集性高、可分性差的高速空间目标群饱和攻击问题，实现非合作空间群目标数量和位置的尽早分辨，该文基于随机有限集(RFS)理论和动力学方程约束研究了空间“团状”目标数量和位置分辨问题，提出目标监测早期解决大量距离靠近、运动特征差异不明显的高速空间群目标数量和位置估计的相关算法，该算法利用概率假设密度(PHD)滤波器能够解决未知时变环境下目标个数与状态估计的特点，将高斯混合PHD (GM-PHD)滤波和空间目标动力学方程相结合，在解决不可分辨空间群目标数量和位置估计问题的同时，充分利用空间目标动力学方程对群内目标状态进行实时调整，提高空间目标位置状态估计精度，解决不可分辨空间目标群边跟踪边分辨问题，相关算法可为空间群目标数量和群内特殊价值个体目标位置尽快分辨、连续稳定跟踪和可靠动向预报等提供数据基础。

Number and Position Estimation Algorithm of Space Group Targets Based on Probability Hypothesis Density Filter and Dynamic Model

Space targets have the characteristics of wide coverage, fast speed, high target density and similar movement, which lead to that in a relatively long time these targets can not be effectively distinguished. How to distinguish effectively the number and position of these non-cooperative space targets as soon as possible is very important. Therefore, based on Random Finite Set (RFS) theory and dynamic model of space targets, the number and position estimation method of unresolved space group targets is studied in this paper, which can effectively estimate the number and position of space group targets with high-speed and small spatial distribution range in the early stage of target monitoring. This method makes full use of the characteristics of Probability Hypothesis Density (PHD) filter, which can solve the number and state estimation of targets in unknown time-varying environment. The Gaussian Mixture PHD (GM-PHD) filter is combined with the space target dynamic equation to estimate the number of unresolved space targets, and the target state are estimated more effectively by the constraint of the dynamic equation. At the same time of target tracking, the resolution problem of unresolved space group targets can be solved. The correlation algorithms can provide data basis for state estimation, continuous stable tracking and reliable trajectory prediction of special value individual target in the group.