摘要
针对杂波环境下的多目标跟踪,概率假设密度(probability hypothesis density,PHD)滤波不能提供目标航迹信息的问题,提出一种基于PHD滤波和数据关联的多目标跟踪方法。利用PHD滤波消除杂波并得到各个时刻的目标个数和目标状态估计。将PHD滤波的结果重新定义为量测数据,通过数据关联进一步消除虚警和漏警并给出目标航迹。仿真结果表明,该算法可以在有效地提高杂波环境下多目标跟踪精度的同时提供各目标航迹信息。
For multi-target tracking in clutter,the probability hypothesis density（PHD） filter can not provide the track information of each target,thus,a novel method based on PHD filter and data association is proposed.Firstly,the PHD filter is utilized to remove the clutters and get the estimated target number and states at each time step.Then,the results of PHD filter are redefined as the measurements,which are managed by data association so that the false alarms and miss detections are further eliminated and the tracks of targets are provided.Simulation results demonstrate that the proposed method can improve the accuracy of multi-target tracking effectively as well as provide track information of each target.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2011年第4期734-737,共4页
Systems Engineering and Electronics
基金
国家自然科学基金(60972159
61032001)
航空科学基金(20085184003)资助课题
关键词
概率假设密度
数据关联
多目标跟踪
随机有限集
最近邻域标准滤波器
probability hypothesis density（PHD）
data association
multi-target tracking
random finite set（RFS）
nearest neighbor standard filter（NNSF）
