改进的K-Means算法在特征关联中的应用  

A Novel Algorithm for Feature Association Based on Gray Correlation Cluster

作　　者：关欣[1] 孙祥威[1] 曹昕莹[1] 

机构地区：[1]海军航空工程学院信息融合技术研究所,山东烟台264001

出　　处：《雷达科学与技术》2014年第1期81-85,共5页Radar Science and Technology

基　　金：新世纪优秀人才支持计划(No.NCET-11-0872)

摘　　要：特征关联是无源多传感器辐射源融合识别的一个关键步骤。特征关联是根据来源于同一辐射源的量测数据所具有的相似性,采用一定的算法和分配策略将多传感器获取的对多辐射源的量测值进行分类划分和关联判定,利用辐射源的特征信息来消除关联模糊。特征关联过程中一个重要环节就是分类算法的选取。K-Means算法是基于划分的聚类算法,已经广泛应用于诸多领域。改进了K-Means算法,用灰关联度代替传统的距离度量定义了样本点间的距离,并对模拟产生的雷达辐射源特征参数样本集Radar-database进行了分类。仿真结果表明,改进的K-Means算法提高了关联正确率,但消耗了更多时间。Feature association is a critical step of radar emitter fusion identification through passive multi-sensor system.The feature association is based on the similarity of measurement data from the same sources of radiation.The measurement values of multiple radiation sources achieved by the passive multi-sen-sor system is classified and relevance determined and the feature information of the radiation sources is used to eliminate interconnected fuzzy.The selection of classification algorithms is an important part of the process of feature association.The K-Means clustering algorithm is based on the division algorithm and has been widely used in many fields.The proposed improved K-Means algorithm defines the similarity between sample points by grey relational degree instead of the traditional distance measure,then the simulation Radar-data-base sample set of radar emitter feature parameters is classified by the improved K-Means algorithm.Simula-tion results show that the improved K-Means algorithm improves the correct rate of association,but con-sumes more time.

关 键 词：K-MEANS算法 无源多传感器 特征关联 灰关联度 

分 类 号：TN974[电子电信—信号与信息处理]