基于快速支持向量聚类和相似熵的多参雷达信号分选方法

doi:10.3724/SP.J.1146.2011.00261

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摘要该文针对现有聚类算法在雷达信号分选应用中复杂度高，准确性低的问题，研究了一种基于锥面簇分配的支持向量聚类算法，该算法在数据空间进行簇分配，避免了特征空间中计算邻接矩阵带来的高复杂度问题。该文将此算法引入雷达信号分选中，并在此基础上对其进行改进，使改进后的算法能对异常值做进一步处理，以达到缩短消耗时间的同时提高正确率的目的。同时以信息熵的理论描述类内聚集度和类间分离度，应用相似熵指标验证分选效果的有效性。仿真结果表明，该方法在提高分选正确率的同时可以有效降低计算复杂度。

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	王世强
	张登福
	毕笃彦
	雍霄驹

关键词 ：雷达信号分选, 支持向量聚类, 锥面簇分配, 相似熵指标

Abstract：The radar signal sorting method based on traditional clustering algorithm takes on a high time complexity and has poor accuracy. Considering the issue, a new sorting method is researched based on Cone Cluster Labeling (CCL) method for Support Vector Clustering (SVC) algorithm. The CCL method labels cluster in data space, and therefore avoides the high complexity caused by the calculation of adjacency matrix in feature space. This method is introduced into the radar signal sorting and it is modified for lower complexity and high accuracy by handling the outliers. Meanwhile a new cluster validity index, Similitude Entropy (SE) index, is proposed which assesses the compactness and separation of clusters using information entropy theory. Experimental results show that the strategy can improve efficiency without sacrificing sorting accuracy.

Key words： Radar signal sorting Support Vector Clustering (SVC) Cone Cluster Labeling (CCL) Similitude Entropy (SE) index

收稿日期: 2011-03-21

PACS:

TN974

基金资助:

国防科技重点实验室基金(9140C610301080C6106)资助课题

通讯作者: 王世强 E-mail: wunsicon@163.com

引用本文:

王世强, 张登福, 毕笃彦, 雍霄驹. 基于快速支持向量聚类和相似熵的多参雷达信号分选方法[J]. 电子与信息学报, 2011, 33(11): 2735-2741. Wang Shi-Qiang, Zhang Deng-Fu, Bi Du-Yan, Yong Xiao-Ju. Multi-parameter Radar Signal Sorting Method Based on Fast Support Vector Clustering and Similitude Entropy. , 2011, 33(11): 2735-2741.

链接本文:

http://jeit.ie.ac.cn/CN/10.3724/SP.J.1146.2011.00261 或 http://jeit.ie.ac.cn/CN/Y2011/V33/I11/2735