多帧高斯混合概率假设密度的多目标跟踪算法  

Multi-frame Gaussian Mixture Probability Hypothesis Density Algorithm for Multi-target Tracking

作　　者：高丽[1] 卢娜[1] GAO Li;LU Na(Shangqiu Polytechnic,Shangqiu 476000,China)

机构地区：[1]商丘职业技术学院,河南商丘476000

出　　处：《火力与指挥控制》2017年第9期93-97,共5页Fire Control & Command Control

基　　金：河南省高等学校重点科研基金资助项目(16A520066;17A520052)

摘　　要：针对低检测概率下多目标跟踪时,概率假设密度滤波器难以正确估计当前目标个数以及目标状态问题,提出一种基于多帧融合的高斯混合概率假设密度滤波算法。根据不同时刻目标权值构造目标多帧权值记录集及目标状态抽取标志。当某些时刻目标被漏检时,依据目标状态抽取标志,并结合目标多帧权值记录集中权值信息估计丢失目标的状态。仿真实验表明,算法有效地提高了低检测概率下现有相关算法的目标状态和数目估计精度。For the problem of the probability hypothesis density(PHD)incapable of correctlyestimating target states and their number in low probability of detection multi-target tracking scenario,a multi-frame fusion-based Gaussian mixture PHD algorithm is proposed.According to the weights oftargets at each time step,both the multi-frame weight recorder and state extraction flag of individualtargets are constructed.When the targets are undetected at some times,the states of these lost targetsare extracted based on multi-frame weight recorder and state extraction flag of each target.Simulationresults show that the proposed algorithm effectively improve the performance of the existing relatedalgorithms in terms of target states and their number in low detection probability scenarios.

关 键 词：多目标跟踪 高斯混合 概率假设密度 多帧 状态提取 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]