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

作　　者：韩继辉 高龙 黄子奇 黄道颖 张安琳 HAN Jihui;GAO Long;HUANG Ziqi;HUANG Daoying;ZHANG Anlin(College of Computer Science and Technology,Zhengzhou University of Light Industry,Zhengzhou 450001,China;North Information Control Research Academy Group Co.,Ltd.,Nanjing 211153,China;Engineering Training Center,Zhengzhou University of Light Industry,Zhengzhou450001,China)

机构地区：[1]郑州轻工业大学计算机科学与技术学院,郑州450001 [2]北方信息控制研究院集团有限公司,南京211153 [3]郑州轻工业大学工程训练中心,郑州450001

出　　处：《火力与指挥控制》2024年第12期62-67,76,共7页Fire Control & Command Control

基　　金：国家科技支撑计划基金资助项目(2006BAK01A38)。

摘　　要：针对杂波环境下联合概率数据关联算法(joint probabilistic data association,JPDA)计算复杂度较高、实时性较差等问题,提出一种基于最大熵模糊聚类的JPDA算法。基于目标轨迹和量测之间的关联规则,采用最大熵模糊聚类算法实现量测与目标的初步数据关联,分析了公共量测对目标跟踪的影响,并引入了公共量测影响系数来修正关联概率,最后使用卡尔曼滤波算法对目标的状态估计进行预测,从而更新各个目标的状态。仿真结果表明,所提算法有效解决了在密集杂波环境中JPDA算法组合爆炸问题,极大缩短计算时间,提高了算法的实时性。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 

分 类 号：TP27[自动化与计算机技术—检测技术与自动化装置]