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

 ORCID:

Ping Sui

http://orcid.org/0000-0001-9132-9431

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

http://doi.org/10.1631/FITEE.1800025


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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Abstract: 
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.

基于核空间投影和联合表征的跳频信号辐射源指纹特征识别

摘要:跳频作为扩频通信的一种常用技术,以其截获概率低、抗干扰能力强和保密性好等优点,在雷达和通信系统中得到广泛应用。然而,非合作条件下的跳频信号辐射源识别作为一大难题,不仅由于其对噪声影响敏感,信号的非线性、非高斯性和非平稳性使得很难在其原始信号空间实现跳频信号分类识别。现有的一些分类识别算法,如稀疏表征分类算法(SRC),仅使用单个信号样本而非整体样本表征测试数据,过分强调稀疏特性而忽略信号样本之间的相关性。为解决上述问题,本文提出一种基于核空间投影和联合表征的跳频信号辐射源指纹特征识别方法。该方法将核空间投影、相关特性表征以及个体分类学习融合到同一个联合表征框架,通过该框架实现辐射源信号分类识别。实际跳频信号的大量实验表明,与几种最先进的识别方法相比,所提算法具有可行性和有效性。

关键词:跳频信号;指纹特征;核函数;联合表征;辐射源识别

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