摘要
基于支持向量聚类,提出一种新的针对未知雷达辐射源信号分选的算法。该方法在原有支持向量的基础上,在聚类标识阶段通过用支持向量点代替原来的全部样本点来进行聚类分配,减少了关联矩阵的规模,从而有效节省了聚类分配的时间,提高了运行速度。同时结合合并同类聚类中心算法有效缓解了核函数的参数q对聚类结果的影响,使得聚类精度有了一定的提高。对未知雷达辐射源信号进行聚类分析的数值实验结果表明,该改进支持向量(SVC)算法不仅显著改善了SVC算法的时间性能,而且具有较高的识别率。
Based on support vector clustering,a new algorithm for unknown radar signal for unknown sor- ting is introduced. The method improves the traditional support vector clustering algorithm during the cluster assignment phase ,using support vector instead of all samples of data sets to reduce the incidence matrix of the scale,which effective to shorten the operational time and raise the operational speed. At the same time, the cen- tres merged together algorithm is used to relieve nuclear function parameters q to affect the outcome, which make clustering precision has become much. The unknown radar signal clustering of the experiment results show that the improvement of clustering algorithm not only improves the performance but also has a higher rate of identification.
出处
《航天电子对抗》
2011年第1期50-53,共4页
Aerospace Electronic Warfare
关键词
雷达信号分选
支持向量聚类
radar signal sorting~ support vector clustering
