火力与指挥控制2026，Vol.51Issue(1)：42-48,7.DOI:10.3969/j.issn.1002-0640.2026.01.005
密集杂波下的双门限核化聚类JPDA算法
The Dual-threshold Kernelized Clustering Joint Probabilistic Data Association Algorithm for Dense Clutter Scenarios
摘要
Abstract
Aiming at the"association explosion"problem of the Joint Probabilistic Data Association(JPDA)algorithm,a Dual-Threshold Kernelized Clustering-based JPDA(DTKC-JPDA)algorithm is proposed.Firstly,a velocity tracking gate is introduced on the basis of the elliptical tracking gate combined with the target velocity constraints to reduce the number of measurements falling within the tracking gate.Secondly,the kernel functions are utilized to map the data into a high-dimensional space,and the constraints on the membership degree are relaxed.Finally,a common-measurement correction factor is introduced to adjust the association probability of the common measurements.The simulation results demonstrate that the algorithm achieves improvements in both operational efficiency and tracking accuracy.关键词
数据关联/目标跟踪/联合概率数据关联算法/核函数/模糊聚类Key words
data association/target tracking/JPDA algorithm/kernel functions/fuzzy clustering分类
信息技术与安全科学引用本文复制引用
常金瑞,张安琳,黄子奇,韩继辉,黄道颖..密集杂波下的双门限核化聚类JPDA算法[J].火力与指挥控制,2026,51(1):42-48,7.基金项目
国家科技支撑计划资助项目(2006BAK01A38) （2006BAK01A38）
