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Wireless Communications and Mobile Computing
Volume 2017 (2017), Article ID 9403590, 9 pages
https://doi.org/10.1155/2017/9403590
Research Article

Frequency-Hopping Transmitter Fingerprint Feature Classification Based on Kernel Collaborative Representation Classifier

Institute of Information and Navigation, Air Force Engineering University, Xi’an, Shaanxi 710077, China

Correspondence should be addressed to Ping Sui; moc.361@nixgninuwiz

Received 15 June 2017; Accepted 27 August 2017; Published 9 October 2017

Academic Editor: Donatella Darsena

Copyright © 2017 Ping Sui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.