摘要
针对传统k-means聚类算法在雷达信号分选中应用存在的不足,提出了一种基于数据场和灰关联分析的k-means聚类雷达信号分选算法。该算法首先根据数据场理论计算所有数据样本的势值,寻找局域势值最大值,选取距最大值最近的样本数据作为初始聚类中心,局域势值最大值个数作为聚类数目;然后用灰关联度代替欧式距离来判断数据样本间相似性。该算法能够自动获取初始聚类中心和聚类数目,对频率捷变雷达具有较好的分选效果。仿真结果验证了算法的可行性。
For the defects in algorithm, a radar signal sorting grey relational analysis. Firstly based on data field theory, to fin the application of radar signal sorting of the tradition k-means clustering algorithm of k-means clustering is put forward based on data field and potential value of all the data samplesis calculated with the algorithm d cent sample data as the initial clustering center, the number of local maximum potential value as the number of clustering. Then grey relational degree is used to determine the similarity between data sample instead of Euclidean. The algorithm can automatically obtain the initial clustering center and clustering number, so it has a good sort effect of frequency agility radar. The simulation results verify the feasibility of this algorithm.
出处
《现代防御技术》
北大核心
2015年第6期136-141,共6页
Modern Defence Technology
