基于滑窗MCMC的弹道导弹分导团目标数据关联研究

弹道导弹在再入过程中为了提高自身突防能力往往伴随着分导现象。由于分导弹头数目未知,距离目标近且再入速度非常相近,使其以团状形态运动,在未知导弹任何先验信息前提下如何对分导弹头进行快速关联已成为亟待解决的难题。该文提出了一种改进的实时滑窗马尔可夫链-蒙特卡洛(Markov Chain Monte Carlo,MCMC)次优数据关联算法,它应用蒙特卡洛采样方法对监控区域的测量集合进行组合优化,获得最大的后验概率密度进而逼近马氏链的平稳分布。该算法结合弹头分导实际情况,重新分配关联假设权值并优化了继承性,极大地减小了关联时间。仿真结果表明该算法与经典的多假设算法相比,关联概率随着目标密集程度增加而显著提高,并且计算量远小于多假设算法。

Research on Group Data Association of Ballistic Missiles Warhead Separation Based on Sliding Window MCMC

It is a common issue for Ballistic Missile(BM) to separate warheads in order to improve the penetration probability during reentry phase.For the reasons of unknown warhead number,closeness between target and warheads and similarity of reentry velocities of the warheads which make them moving as a group,how to rapidly associate the separating warheads without any prior information has become an urgent problem.This paper proposes an improved real-time sliding window Markov Chain-Monte Carlo(MCMC) suboptimal association algorithm.By calculating the maximum posterior probability of combination from the surveillance area observations using Monte Carlo method,the algorithm approximates the Markov Chain’s stable distribution.Furthermore,considering the warhead separation reality,the sliding window MCMC reassigns the weights of the probability association hypothesis and optimizes the inheritance yielding greatly reduction in computation.Simulation results show that the proposed algorithm yields significant improvements both in association and computation performance under heavy dense targets compared with classical Multiple Hypothesis Tracking(MHT).