Multi-feature fusion classification method for communication specific emitter identification

Zunwen HE; Shuai HOU; Wancheng ZHANG; Yan ZHANG

doi:10.11959/j.issn.1000-436x.2021028

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Multi-feature fusion classification method for communication specific emitter identification

Papers | 更新时间：2024-06-05

- Multi-feature fusion classification method for communication specific emitter identification
- Journal on Communications Vol. 42, Issue 2, Pages: 103-112(2021)
- 作者机构：
  
  北京理工大学信息与电子学院，北京 100081
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61871035)
- DOI：10.11959/j.issn.1000-436x.2021028
  CLC： TN911.7
- Online First：2021-02，
  
  Published：25 February 2021
- 稿件说明：
移动端阅览
Zunwen HE, Shuai HOU, Wancheng ZHANG, et al. Multi-feature fusion classification method for communication specific emitter identification[J]. Journal on Communications, 2021, 42(2): 103-112.
DOI：

Zunwen HE, Shuai HOU, Wancheng ZHANG, et al. Multi-feature fusion classification method for communication specific emitter identification[J]. Journal on Communications, 2021, 42(2): 103-112. DOI： 10.11959/j.issn.1000-436x.2021028.

摘要

针对通信辐射源个体识别问题，提出了一种基于多通道变换投影、集成深度学习和生成对抗网络的融合分类方法。首先，通过对原始信号进行多种变换得到三维特征图像，据此构建信号的时频域投影以构建特征数据集，并使用生成对抗网络对数据集进行扩充。然后，设计了一种基于多特征融合的双阶段识别分类方法，利用神经网络初级分类器分别对3类特征数据集进行学习，得到初始分类结果。最后，通过叠加融合学习初始分类结果，得到最终的分类结果。实测数据分析结果证明，所提方法相比基于单一特征提取方法和经典多特征提取方法有更高的准确率，使用室外典型场景多径衰落信道模型对辐射源信号进行了处理，所提模型仍可进行有效识别，能够适用于复杂无线信道环境的应用。

Abstract

A multi-feature fusion classification method based on multi-channel transform projection

integrated deep learning and generative adversarial network (GAN) was proposed for communication specific emitter identification.First

three-dimensional feature images were obtained by performing various transformations

the time and frequency domain projection of the signal was constructed to construct the feature datasets.GAN was used to expand the datasets.Then

a two-stage recognition and classification method based on multi-feature fusion was designed.Deep neural networks were used to learn the three feature datasets

and the initial classification results were obtained.Finally

through fusion and re-learning of the initial classification result

the final classification result was obtained.Based on the measurement and analysis of the actual signals

the experimental results show that the method has higher accuracy than the single feature extraction method.The multipath fading channel has been used to simulate the outdoor propagation environment

and the method has certain generalization performance to adapt to the complex wireless channel environments.

关键词

Keywords

references

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