Full Text:   <557>

Summary:  <133>

CLC number: TN911.72

On-line Access: 2019-08-29

Received: 2018-01-09

Revision Accepted: 2018-04-13

Crosschecked: 2019-08-14

Cited: 0

Clicked: 1523

Citations:  Bibtex RefMan EndNote GB/T7714


Ping Sui


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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.8 P.1133-1146


Frequency-hopping transmitter fingerprint feature recognition with kernel projection and joint representation

Author(s):  Ping Sui, Ying Guo, Kun-feng Zhang, Hong-guang Li

Affiliation(s):  Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China

Corresponding email(s):   ziwuningxin@163.com

Key Words:  Frequency-hopping, Fingerprint feature, Kernel function, Joint representation, Transmitter recognition

Ping Sui, Ying Guo, Kun-feng Zhang, Hong-guang Li. Frequency-hopping transmitter fingerprint feature recognition with kernel projection and joint representation[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1133-1146.

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A1 - Ping Sui
A1 - Ying Guo
A1 - Kun-feng Zhang
A1 - Hong-guang Li
J0 - Frontiers of Information Technology & Electronic Engineering
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DOI - 10.1631/FITEE.1800025

frequency-hopping (FH) is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems because of its inherent capability of low interception, good confidentiality, and strong anti- interference. However, non-cooperation FH transmitter classification is a significant and challenging issue for FH transmitter fingerprint feature recognition, since it not only is sensitive to noise but also has non-linear, non-Gaussian, and non-stability characteristics, which make it difficult to guarantee the classification in the original signal space. Some existing classifiers, such as the sparse representation classifier (SRC), generally use an individual representation rather than all the samples to classify the test data, which over-emphasizes sparsity but ignores the collaborative relationship among the given set of samples. To address these problems, we propose a novel classifier, called the kernel joint representation classifier (KJRC), for FH transmitter fingerprint feature recognition, by integrating kernel projection, collaborative feature representation, and classifier learning into a joint framework. Extensive experiments on real-world FH signals demonstrate the effectiveness of the proposed method in comparison with several state-of-the-art recognition methods.




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