弹道目标进入中段飞行时释放弹头及大量诱饵形成密集目标群, 对星载红外像平面的目标跟踪提出新的挑战.针对像平面对目标群各目标分辨个数的时变性, 及各目标像平面轨迹非线性程度强的特点, 提出基于随机有限集的目标群像平面跟踪方法.随机有限集为最优贝叶斯多目标跟踪提供统一的理论基础, 但直接递推多目标后验分布计算量大, 概率假设密度为多目标后验概率分布的一阶矩, 在随机有限集框架下递推概率假设密度为现实可行的次优多目标跟踪方法.采用序贯蒙特卡罗方法实现多目标概率假设密度递推跟踪滤波,计算所有粒子权值之和估计目标数目, 以k-means方法对粒子集进行聚类提取各目标的状态；最后构建天基光学星座对中段弹道目标群的跟踪仿真场景, 在不同交接跟踪任务、虚警率和目标个数条件下进行对比分析和仿真验证.结果表明,该方法能同时跟踪星载红外像平面上动态变化目标群各目标的状态和数目.

Midcourse ballistic target, releasing warhead and large numbers of decoys and forming dense cluster, presents a new challenge for target tracking with space-based infrared focal plane array ( IRFPA ). The numbers of the identified target vary because of the finite resolution of IRFPA. The target trajectory on IRFPA shows strong nonlinear characteristic. To cope with these problems, a tracking algorithm based on random finite set was proposed. Random finite set is a theoretically unified framework of optimal Bayesian multi-target tracking filter, but the recursion of posterior joint multi-target distribution is not practical in use due to the computational hurdle. Probability Hypothesis Density(PHD) is the first moment of multi-target posterior distribution, and the PHD filter is the suboptimal and practical alternative within the framework of random finite set. Sequential Monte Carlo method was proposed to propagate PHD. The target quantity was estimated by summing up all particle's weight. The k-means method was adopted to cluster PHD to estimate the target states. Scenario simulation was set up, where the algorithm was tested under the conditions of different handover tracking tasks, false alarm rates and target numbers. Simulation results show that, the algorithm can simultaneously track the numbers and states of the targets among midcourse ballistic target group on space-based IRFPA.