可提取衍生目标的带标签GM-PHD算法  

Labeling GM-PHD Filter with Spawning Targets

作　　者：陈金广[1] 赵甜甜[1] 马丽丽[1] 徐步高[1,2] 

机构地区：[1]西安工程大学计算机科学学院,西安710048 [2]德克萨斯州大学奥斯汀分校人类生态系,德克萨斯州奥斯汀78712

出　　处：《光电工程》2016年第12期79-84,共6页Opto-Electronic Engineering

基　　金：国家自然科学基金项目(61201118);陕西省自然科学基础研究计划项目(2016JM6030);西安工程大学研究生创新基金项目(CX201631);西安工程大学学科建设项目

摘　　要：针对带标签的高斯混合概率假设密度滤波算法无法获取衍生目标的问题,提出一种可以提取衍生目标的带标签GM-PHD算法。首先,通过为高斯项加注标签的方式区别不同的目标,以辨别单个目标及其航迹。其次,在滤波过程中,对每一时刻得到的状态估计值与已形成的航迹标签进行匹配关联,实现航迹维持。最后,通过设置衍生阈值来判断状态估计中是否存在衍生目标以及可能产生的目标个数,为新生目标高斯项和可能的衍生目标高斯项重新分配标签,并创建新的航迹。仿真实验结果表明,与传统的带标签GM-PHD算法相比,在衍生目标存在的情况下,改进算法具有更好的跟踪性能。The Labeling Gaussian Mixture Hypothesis Probability Density filter （LGM-PHD） cannot get the spawn targets. Addressing this problem, an improved algorithm is presented. Firstly, the labels are applied to the Gaussian items in the GM-PHD filter to distinguish different targets, and their tracks are determined. After that, in the period of filtering, the track labels between the current step and former step are matched, associated and maintained. Finally, the spawn threshold is used to determine if there are spawn targets or not and determine the number of possible spawn targets, then the labels for Gaussian items of new targets and possible spawn targets are reallocated. The simulation results show, in the situation of existing spawn targets, the improved algorithm has better tracking performance than the LGM-PHD.

关 键 词：概率假设密度滤波 随机有限集 状态估计 衍生目标 带标签GM-PHD 

分 类 号：TP931[自动化与计算机技术]