基于联合多目标概率密度模型的多目标检测前跟踪算法

Multi-target track-before-detect algorithm based on joint multi-target probability density model

针对多目标环境下的检测前跟踪问题,提出了基于联合多目标概率密度(JMPD)模型的检测前跟踪(TBD)算法。JMPD模型同时模拟目标数目及其联合状态,采用粒子滤波递归估计JMPD实现目标数目及其状态的估计。仿真实验表明,所提算法在较小的延时检测的情况下,能准确估计目标的出生及消亡,并且航迹跟踪精确稳定,实现了对多个微弱目标的检测及跟踪。

Concerning the problem of Track-Before-Detect（TBD） in a multi-target environment,in this paper,a TBD algorithm based on Joint Multi-target Probability Density（JMPD） model was proposed.The JMPD was a single probabilistic entity that captured uncertainty about the number of targets present in the surveillance region as well as their individual states and a Particle Filter（PF） was used to recursively estimate the JMPD.The simulation results demonstrate that the birth and death of target can be estimated accurately as well as its trajectory by the proposed algorithm with smaller detection delays.