基于核稀疏保持投影的SAR目标特征提取方法研究  

Research on SAR Target Feature Extraction Method Based On Kernel Sparsity Preserving Projections

作　　者：王欢 熊水金[1] 陈荣华[1] WANG Huan;XIONG Shuijin;CHEN Ronghua(Jiangxi Vocational College of Finance and Economics,Jiujiang 332000,China)

机构地区：[1]江西财经职业学院,江西九江332000

出　　处：《现代信息科技》2023年第21期20-23,27,共5页Modern Information Technology

基　　金：江西省教育厅科学技术研究项目(GJJ2204914)。

摘　　要：文章提出一种新的特征提取方法,将核稀疏保持投影(KSPP)方法运用到合成孔径雷达(SAR)目标识别中。该方法将原始目标函数投影到高维特征空间,在高维特征空间求得样本的稀疏系数,将所有样本的稀疏系数组成稀疏重构矩阵,利用稀疏重构矩阵构造目标函数求得样本的特征向量,最后利用SVM分类器对目标进行分类识别。基于MSTAR提供的实测SAR数据对方法进行验证,结果表明该方法能够有效地提高目标识别结果,且对目标的方位角不敏感,是一种有效的SAR目标特征提取方法。This paper proposes a new feature extraction method,which applies Kernal Sparsity Preserving Projections(KSPP)to Sythentic Aperture Radar(SAR)target recognition.In this method,the original objective function is projected into the high-dimensional feature space,and the sparse coefficients of the samples are obtained in the high-dimensional feature space.The sparse coefficients of all samples are formed into the sparse reconstruction matrix,and the sparse reconstruction matrix is used to construct the objective function to obtain the feature vector of the samples.Finally,the SVM classifier is used to classify and recognize the targets.Based on the measured SAR data provided by MSTAR,the proposed method is verified.The results show that this method can effectively improve the target recognition results and is insensitive to the target azimuth angle,so it is an effective method for SAR target feature extraction.

关 键 词：核稀疏保持投影 特征提取 SAR SVM分类器 MSTAR 

分 类 号：TN957.52[电子电信—信号与信息处理]