西南交通大学学报2017，Vol.52Issue(2)：340-347,8.DOI:10.3969/j.issn.0258-2724.2017.02.018
基于自适应聚概率矩阵的JPDA算法研究
Joint Probabilistic Data Association Algorithm Based on Adaptive Cluster Probability Matrix
摘要
Abstract
A novel JPDA method for data association on multi-target tracking system was presented for reducing the class of JPDA algorithm computational complexity and solving the problem of coalesce neighboring tracks.To improve the computational complexity,the joint association event probabilities were calculated with Cheap JPDA algorithm,then the cluster probability matrix was reconstructed by thresholding method to further optimize the computational complexity.Finally,the measurement prone to make wrong association were eliminated by measurement adaptive cancellation method to avoid the track coalescence problem for neighboring tracks.Theoretical analysis and simulation results showed that the proposed algorithm was able to reduce the complexity of the algorithm and improve the timeliness on the basis of preserving the tracking accuracy,and it was also capable of avoiding track coalescence with less errors when tracking the neighboring tracks and cross tracks,comparing with the standard JPDA and Scaled JPDA algorithm.关键词
航迹合并/经验JPDA/聚概率矩阵/阈值处理Key words
track coalescence/cheap JPDA/cluster probability matrix/thresholding method分类
信息技术与安全科学引用本文复制引用
李首庆,徐洋..基于自适应聚概率矩阵的JPDA算法研究[J].西南交通大学学报,2017,52(2):340-347,8.基金项目
国家自然科学基金委员会-中国民用航空局联合研究基金资助项目(U1433126)中国民用航空飞行学院面上项目支持(J2015-1). （U1433126）
