火力与指挥控制2024，Vol.49Issue(12)：62-67,76,7.DOI:10.3969/j.issn.1002-0640.2024.12.007
基于最大熵模糊聚类简化的联合概率数据关联算法
Simplified Joint Probability Data Association Algorithm Based on Maximum Entropy Fuzzy Clustering
摘要
Abstract
Aiming at the problems of high computational complexity and poor real-time perfor-mance of Joint Probabilistic Data Association(JPDA)in clutter environment,a JPDA algorithm based on maximum entropy fuzzy clustering is proposed.First,based on the association rules between the target trajectory and the measurement,the maximum entropy fuzzy clustering algorithm is used to realize the preliminary data association between the measurement and the target.Secondly,the impact of public measurement on target tracking is analyzed,and the public measurement impact fac-tor is introduced to modify the association probability.Finally,the state estimates of the targets are predicted using the Kalman filtering algorithm to update the state of each target.The experimental results show that the algorithm in this paper effectively solves the problem of JPDA algorithm combi-nation explosion in dense clutter environment,greatly shortens the calculation time,and improves the real-time performance of the algorithm.关键词
多目标跟踪/联合概率数据关联算法/最大熵模糊聚类Key words
data association/maximum entropy fuzzy clustering/joint probabilistic data association分类
信息技术与安全科学引用本文复制引用
韩继辉,高龙,黄子奇,黄道颖,张安琳..基于最大熵模糊聚类简化的联合概率数据关联算法[J].火力与指挥控制,2024,49(12):62-67,76,7.基金项目
国家科技支撑计划基金资助项目(2006BAK01A38) （2006BAK01A38）
