自动化学报2017，Vol.43Issue(2)：238-247,10.DOI:10.16383/j.aas.2017.c150881
均方根嵌入式容积粒子PHD多目标跟踪方法
Square-root Imbedded Cubature Particle PHD Multi-target Tracking Algorithm
摘要
Abstract
Considering the low accuracy,filter divergence and other problems of nonlinear multi-target tracking based on probability hypothesis density (PHD),a new filter named advanced square-root imbedded cubature particle PHD (ASRICP-PHD) is proposed.ASRICP-PHD divides the whole particle sampling area into several parts of equal probability,then uses a special rule to obtain particles from each part,and matches the important density function with square-root imbedded cubature particle filter,and therefore predicts and updates PHD.Simulation shows that ASRICP-PHD is able to track multiple targets effectively.Moreover,compared with quasi random sampling,the method of particle sampling based on probability has higher accuracy in terms of multi-target positions and number's estimations.关键词
多目标跟踪/概率假设密度/均方根嵌入式容积滤波/等概率采样Key words
Multi-target tracking/probability hypothesis density (PHD)/square-root imbedded cubature filter/sampling with equal probability引用本文复制引用
熊志刚,黄树彩,赵炜,苑智玮,徐晨洋..均方根嵌入式容积粒子PHD多目标跟踪方法[J].自动化学报,2017,43(2):238-247,10.基金项目
国家自然科学基金(61503408,61573374),陕西省自然科学基础研究计划(2012JM8020),航空科学基金(20130196004)资助 Supported by National Natural Science Foundation of China (61503408,61573374),Natural Science Basic Research Plan in Shaanxi Province of China (2012JM8020),and Aeronautical Science Foundation of China (20130196004) （61503408,61573374）
