改进加权SVC的雷达信号分选新方法

doi:10.3969/j.issn.1009-086x.2018.03.013

现代防御技术 ›› 2018, Vol. 46 ›› Issue (3): 86-92.DOI: 10.3969/j.issn.1009-086x.2018.03.013

改进加权SVC的雷达信号分选新方法

袁泽恒, 田润澜, 张旭洲

空军航空大学航空作战勤务学院, 吉林长春 130022

出版日期:2018-05-30 发布日期:2020-10-22
作者简介:袁泽恒(1995-), 男, 山西垣曲人。硕士生, 主要研究方向为电子侦察情报的分析与处理。
通信地址:130022 吉林省长春市南湖大路2222号 E-mail:yuanzeheng@sina.com

New Method of Radar Signals Sorting Based on Improved Weighted SVC

YUAN Ze-heng, TIAN Run-lan, ZHANG Xu-zhou

Aviation University of Air Force, School of Aviation Operations and Services, Jilin Changchun 130022, China

Online:2018-05-30 Published:2020-10-22

摘要/Abstract

摘要： 目前基于多参数的雷达信号聚类分选方法得到了广泛应用,但是当雷达信号严重交叠时,存在正确率不高的问题,为此,在支持向量聚类和分层互耦的分选算法基础上,首先利用变精度粗糙集对标准化的雷达信号数据进行加权处理,然后通过分析聚类分选结果构建有效性评价模型,确定最佳的聚类分选参数。仿真表明,当雷达信号数据严重交叠时,相比原始方法,改进的方法正确率显著提高,证明了方法的有效性。

关键词: 雷达信号分选, 支持向量聚类 , 变精度粗糙集, 加权处理, 有效性评价模型, 聚类分选参数

Abstract: At present, multi parameter radar signal clustering is widely used, however, when the radar signals are heavily overlapped, there is a problem of low accuracy. Based on support vector clustering and delaminating coupling sorting algorithm, the standardized radar signal data are weighted by the variable precision rough set; by analyzing the results of cluster sorting, the model of effective evaluation is established; and the optimal clustering sorting parameters are determined. The simulation results show that when the radar signal data is seriously overlapped, the accuracy of the proposed method is improved greatly compared with the original method, which proves the effectiveness of the proposed method.

Key words: radar signal sorting, support vector clustering, variable precision rough sets, weighted processing, effective evaluation model, clustering sorting parameter

中图分类号:

TN974

袁泽恒, 田润澜, 张旭洲. 改进加权SVC的雷达信号分选新方法[J]. 现代防御技术, 2018, 46(3): 86-92.

YUAN Ze-heng, TIAN Run-lan, ZHANG Xu-zhou. New Method of Radar Signals Sorting Based on Improved Weighted SVC[J]. Modern Defense Technology, 2018, 46(3): 86-92.

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改进加权SVC的雷达信号分选新方法

New Method of Radar Signals Sorting Based on Improved Weighted SVC

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