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

Ping SUI; Ying GUO; Kun-feng ZHANG; Hong-guang LI

doi:10.1631/FITEE.1800025

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

Regular Papers | Updated：2022-05-19

- Frequency-hopping transmitter fingerprint feature recognition with kernel projection and joint representation
- 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
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 8, Pages: 1133-1146(2019)
- Affiliations：
  
  Institute of Information and Navigation, Air Force Engineering University, Xi'an 710077, China
- Author bio：
  
  Ping SUI, E-mail: ziwuningxin@163.com
- Funds：
- DOI：10.1631/FITEE.1800025
  CLC： TN911.72
- Received：09 January 2018，
  
  Revised：2019-;8-14，
  
  Published：2019-08
- Accepted：
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Ping SUI, Ying GUO, Kun-feng ZHANG, et al. Frequency-hopping transmitter fingerprint feature recognition with kernel projection and joint representation[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1133-1146.
DOI：

Ping SUI, Ying GUO, Kun-feng ZHANG, et al. Frequency-hopping transmitter fingerprint feature recognition with kernel projection and joint representation[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1133-1146. DOI： 10.1631/FITEE.1800025.

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。

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.

关键词

Keywords

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