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基于核聚类的机动多目标数据关联问题研究
引用本文:杨新刚,刘以安,韩双.基于核聚类的机动多目标数据关联问题研究[J].计算机工程与设计,2007,28(20):4845-4846,4849.
作者姓名:杨新刚  刘以安  韩双
作者单位:江南大学,信息工程学院,江苏,无锡,214036
基金项目:国防预研应用基础研究基金
摘    要:针对新型作战样式条件下空中多机动目标密集回波的数据关联问题,采用核学习方法和K-均值聚类相结合的算法,即基于核的K-均值聚类来解决此问题.该方法的主要思想是,将原空间中的样本通过一个非线性映射,映射到高维的核空间中,以突出各类样本之间的特征差异,然后在核空间中进行K-均值聚类.仿真结果表明,该方法有效提高了密集回波环境下系统跟踪机动多目标的关联精度和可靠性.

关 键 词:K-均值聚类  核聚类  核函数  机动多目标  数据关联  核聚类  机动目标  目标数据  关联问题  研究  based  data  association  精度  多目标  系统跟踪  环境  仿真结果  特征差异  核空间  线性映射  样本  原空间  思想  核学习方法  均值聚类
文章编号:1000-7024(2007)20-4845-02
修稿时间:2006-11-30

Study on data association of multi-maneuvering targets based on kernel-cluster
YANG Xin-gang,LIU Yi-an,HAN Shuang.Study on data association of multi-maneuvering targets based on kernel-cluster[J].Computer Engineering and Design,2007,28(20):4845-4846,4849.
Authors:YANG Xin-gang  LIU Yi-an  HAN Shuang
Affiliation:College of Information Engineering, Southern Yangtze University, Wuxi 214036, China
Abstract:Aiming at the data association problem in high dense multi-return environment,kernel learning method and K-means algorithm,namely the K-means cluster based on kernel,are combined to solve the data association of multi-maneuvering targets.The idea of this algorithm is mapping the sample from the original space to a high dimension kernel space where there are characteristic differences among all kinds of samples,and then perform K-means clustering in this high dimension space.The simulation results indicate that this method can effectively improve the precision and reliability of the system under dense multi-return environment.
Keywords:k-means cluster  kernel cluster  kernel function  multi-maneuvering targets  data association
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