CAJ | 학술논문

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-da-tabase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。
특정관련시무원다전감기복사원융합식별적일개관건보취。특정관련시근거래원우동일복사원적량측수거소구유적상사성，채용일정적산법화분배책략장다전감기획취적대다복사원적량측치진행분류화분화관련판정，이용복사원적특정신식래소제관련모호。특정관련과정중일개중요배절취시분류산법적선취。K-Means산법시기우화분적취류산법，이경엄범응용우제다영역。개진료 K-Means산법，용회관련도대체전통적거리도량정의료양본점간적거리，병대모의산생적뢰체복사원특정삼수양본집 Radar-da-tabase진행료분류。방진결과표명，개진적K-Means산법제고료관련정학솔，단소모료경다시간。
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.