Frequency-Hopping Transmitter Fingerprint Feature Classification Based on Kernel Collaborative Representation Classifier. (9th October 2017)

• Record Type: Journal Article
• Authors: Sui, Ping; Guo, Ying; Zhang, Kun-feng; Li, Honguang
• Other Names: Darsena Donatella Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2017(2017)
• Publication Date: 2017-10-09
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2017/9403590
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Noncooperation frequency-hopping (FH) transmitter fingerprint feature classification is a significant but challenging issue for FH transmitter recognition, since not only is it sensitive to noise but also it has the nonlinear, non-Gaussian and nonstability characteristics, which make it difficult to guarantee the classification in the original signal space. To address these problems, a method of frequency-hopping transmitter fingerprint feature classification based on kernel collaborative representation classifier is proposed in this paper. First, the noise suppression pretreatment of the FH transmitter signal is carried out by using the wave atoms frame method. Then, the nuances of the FH transmitters in the feature space are characterized by the surrounding-line integral bispectra features. And finally, incorporating the kernel function, a classifier which can generalize a linear algorithm to nonlinear counterpart is constructed for the final transmitter fingerprint feature classification. Extensive experiments on real-world FH transmitter "turn-on" transient signals demonstrate the robust classification of our method.