改进的多目标GM-PHD分量融合算法  

Improved Component Merging Algorithm for Multi-target GM-PHD

作　　者：孙志强[1] SUN Zhi-qiang(Department of Mechanical and Electronic Engineering,Shangqiu Polytechnic,Shangqiu 476000,China)

机构地区：[1]商丘职业技术学院机电系,河南商丘476000

出　　处：《火力与指挥控制》2021年第2期109-113,共5页Fire Control & Command Control

基　　金：河南省科技攻关资助项目(182102210116)。

摘　　要：密集杂波的平行多目标跟踪场景中,高斯混合概率假设密度滤波器的计算代价随着分量的增多而不断变大,且其目标状态估计精度较低。为了解决这些问题,基于高斯混合概率假设密度滤波框架,提出一种改进的目标分量融合算法。通过目标分量的权重、均值及协方差的充分协作,该算法能够极大程度地融合目标强度中的相似分量,同时能够有效地避免真实目标分量被错误融合。仿真结果表明,密集杂波环境下该算法不仅具有较高的目标状态估计精度,而且其计算代价相对较低。In parallel multiple target tracking scenes with dense clutters,the computation cost of the Gaussian Mixture Probability Hypothesis Density(GM-PHD)filter varies with the increase of components,and its target state estimate accuracy is low.To overcome these problems,an improved target component merging algorithm under the framework of the GM-PHD filter is presented.By the full collaboration of the weight,mean value and covariance of target component,the proposed algorithm can greatly merge the similar components in the target intensity,and effectively avoid incorrectly fusion of components of real targets.The simulation results show that the proposed algorithm not only has high accuracy of target state estimation but also has a relatively low computation cost in dense clutter environments.

关 键 词：目标跟踪 高斯混合概率假设密度 分量融合 运算代价 

分 类 号：TN953[电子电信—信号与信息处理]