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中文摘要：
为了实现多目标峰值及航迹联合提取，提出了一种基于全局时空信息的序贯蒙特卡洛概率假设密度（SMC-PHD）多目标峰值提取及航迹提取一体化处理方法．该算法利用粒子空间分布信息将粒子聚拢形成粒子簇，构建历史航迹与粒子簇的匹配关系，同时结合粒子权值信息对粒子标签更新，并依据标签演化特性实现目标峰值及航迹提取．仿真结果表明，所提算法具有稳定的跟踪性能，同时目标信息精度得到显著改善．
英文摘要：
A global space-temporal information-based sequential Monte Carlo probability hypothesis density （ SMC- PHD） multi-target tracking algorithm is proposed to jointly extract multi-target peaks and tracks. This algorithm assembles particles into multiple particle clusters based on the particles＇ space distribution, constructs relationship between tracks and clusters, updates padicle labels based on particle weights, and extracts multi- target peaks and tracks according to the evolving characteristics of the particles. Simulation results demonstrate that the proposed algorithm provides a stable tracking performance and significantly improves multi-target information extraction accuracy.
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