雷达学报Issue(4)：406-413,8.DOI:10.3724/SP.J.1300.2012.20094
一种用于多目标跟踪的增强型SMC-PHD滤波算法
An Improved SMC-PHD Filter for Multiple Targets Tracking
摘要
Abstract
Two improved contributions have been advanced for the standard Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) filter. Firstly, a novel method is advanced for the cardinality and state estimation. A weight matrix is firstly calculated by measurements and persistent particles, and the weight sum of each row is then evaluated, the measurements indexed by row will be judged as true if its weight sum is larger than a certain threshold, and the weight sum of persistent particle states will be reported as the true target states. Secondly, an assistant variable which is used to denote the persistent age for every particle is introduced, by the help of this age variable, the overrated problem of targets number in dense clutter environment can be effectively restricted. The results of numerical simulation prove that the improved SMC-PHD filter has higher tracking performance than the standard one.关键词
多目标跟踪/概率假设密度/序贯蒙特卡罗/目标出生强度Key words
Multiple target tracking/Probability Hypothesis Density (PHD)/Sequential Monte Carlo (SMC)/Target birth intensity分类
信息技术与安全科学引用本文复制引用
吴伟,尹成友..一种用于多目标跟踪的增强型SMC-PHD滤波算法[J].雷达学报,2012,(4):406-413,8.基金项目
安徽省自然科学基金(090412067)资助课题 （090412067）
