SEMI-SUPERVISED RADIO TRANSMITTER CLASSIFICATION BASED ON ELASTIC SPARSITY REGULARIZED SVM  

作　　者：Hu Guyu Gong Yong Chen Yande Pan Zhisong Deng Zhantao 

机构地区：[1]Institute of Command Automation,PLA University of Science and Technology [2]Nanjing Army Command College, Nanjing 210045

出　　处：《Journal of Electronics(China)》2012年第6期501-508,共8页电子科学学刊（英文版）

基　　金：Supported by the Hi-Tech Research and Development Program of China (No. 2009AAJ130)

摘　　要：Non-collaborative radio transmitter recognition is a significant but challenging issue, since it is hard or costly to obtain labeled training data samples. In order to make effective use of the unlabeled samples which can be obtained much easier, a novel semi-supervised classification method named Elastic Sparsity Regularized Support Vector Machine (ESRSVM) is proposed for radio transmitter classification. ESRSVM first constructs an elastic-net graph over data samples to capture the robust and natural discriminating information and then incorporate the information into the manifold learning framework by an elastic sparsity regularization term. Experimental results on 10 GMSK modulated Automatic Identification System radios and 15 FM walkie-talkie radios show that ESRSVM achieves obviously better performance than KNN and SVM, which use only labeled samples for classification, and also outperforms semi-supervised classifier LapSVM based on manifold regularization.Non-collaborative radio transmitter recognition is a significant but challenging issue, since it is hard or costly to obtain labeled training data samples. In order to make effective use of the unlabeled samples which can be obtained much easier, a novel semi-supervised classification method named Elastic Sparsity Regularized Support Vector Machine （ESRSVM） is proposed for radio transmitter classification. ESRSVM first constructs an elastic-net graph over data samples to capture the robust and natural discriminating information and then incorporate the information into the manifold learning framework by an elastic sparsity regularization term. Experimental results on 10 GMSK modulated Automatic Identification System radios and 15 FM walkie-talkie radios show that ESRSVM achieves obviously better performance than KNN and SVM, which use only labeled samples for classification, and also outperforms semi-supervised classifier LapSVM based on manifold regu- larization.

关 键 词：Radio transmitter recognition Cyclic spectrum density Semi-supervised classification Elastic Sparsity Regularized Support Vector Machine (ESRSVM) 

分 类 号：TN973.1[电子电信—信号与信息处理]