计算机工程与应用2019，Vol.55Issue(1)：50-55,95,7.DOI:10.3778/j.issn.1002-8331.1805-0090
采用分段RTS的CPHD平滑算法
Cardinalized Probability Hypothesis Density Smoother Using Piece-wise RTS
摘要
Abstract
Aiming at the problem of fixed interval smoothing in multi-target tracking, the Cardinalized Probability Hypothesis Density(CPHD)filter and the RTS smoother are combined, and a cardinalized probability hypothesis density smoothing algorithm for RTS is given. Considering the problem of large output delay in the smoothing process, a piecewise RTS cardinalized probability hypothesized density smoother is proposed using the idea of piecewise smoothing. Firstly, estimation values are segmented using a fixed interval. Secondly, track-estimate is associated using Hungarian algorithm. Finally, the RTS smoothing is performed on the associated tracks. The experimental results show, the CPHD smoother using piecewise RTS can estimate the target state more accurately comparing with the CPHD filter, and can effectively avoid the problem of poor real-time performance when used RTS smoother directly.关键词
目标跟踪/RTS平滑/势概率假设密度滤波/航迹-估计关联/信息融合Key words
target tracking/RTS smoother/Cardinalized Probability Hypothesis Density filter(CPHD)/track-estimate association/information fusion分类
信息技术与安全科学引用本文复制引用
陈金广,王星辉,马丽丽,张馨东,巩林明..采用分段RTS的CPHD平滑算法[J].计算机工程与应用,2019,55(1):50-55,95,7.基金项目
陕西省自然科学基础研究计划(No.2016JM6030) （No.2016JM6030）
陕西省教育厅科研计划(No.18JK0349) （No.18JK0349）
西安工程大学研究生创新基金(No.chx201813). （No.chx201813）
