融合深度特征的电磁频谱异常检测算法

彭闯; 王伦文; 胡炜林

doi:10.12263/DZXB.20210916

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融合深度特征的电磁频谱异常检测算法

电磁频谱智能+ | 更新时间：2025-12-08

- 融合深度特征的电磁频谱异常检测算法
- Spectrum Anomaly Detection Algorithm Based on the Fusion of Depth Feature
- 电子学报 2022年50卷第6期页码：1359-1369
- 作者机构：
  
  国防科技大学电子对抗学院，安徽合肥 230037
- 作者简介：
  
  [ "彭闯男，1994年出生，山东曲阜人.国防科技大学电子对抗学院博士研究生.主要研究方向为深度学习、电磁环境态势认知等.E-mail: pengchuang17@nudt.edu.cn" ]
  [ "王伦文（通讯作者）男，1966年出生，安徽休宁人.国防科技大学电子对抗学院教授，博士生导师.主要研究方向为智能信息处理、数据挖掘、电磁环境态势认知等." ]
  [ "胡炜林男，1997年出生，四川泸州人.国防科技大学电子对抗学院硕士研究生.主要研究方向为电磁态势认知、机器学习等.E-mail: hwl550@nudt.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(11975307);国防科技创新特区项目(19-H863-01-ZT-003-003-12)
- DOI：10.12263/DZXB.20210916
  中图分类号： TN98;
- 收稿：2021-07-15，
  
  修回：2021-12-27，
  
  纸质出版：2022-06-25
- 稿件说明：
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彭闯,王伦文,胡炜林.融合深度特征的电磁频谱异常检测算法[J].电子学报,2022,50(06):1359-1369.

PENG Chuang,WANG Lun-wen,HU Wei-lin.Spectrum Anomaly Detection Algorithm Based on the Fusion of Depth Feature[J].ACTA ELECTRONICA SINICA,2022,50(06):1359-1369.
彭闯,王伦文,胡炜林.融合深度特征的电磁频谱异常检测算法[J].电子学报,2022,50(06):1359-1369. DOI： 10.12263/DZXB.20210916.

PENG Chuang,WANG Lun-wen,HU Wei-lin.Spectrum Anomaly Detection Algorithm Based on the Fusion of Depth Feature[J].ACTA ELECTRONICA SINICA,2022,50(06):1359-1369. DOI： 10.12263/DZXB.20210916.

摘要

针对电磁频谱异常检测效率不高等问题，该文结合卷积神经网络（Convolutional Neural Networks，CNN）以及长短时记忆神经（Long Short-Term Memory，LSTM）网络，提出一种融合深度特征的电磁频谱异常检测算法.首先构建深度特征提取网络，该网络包含能够分级提取深度特征的两路多层CNN以及LSTM；其次通过池化、合并等操作将网络模型提取的各级深度特征进行融合，实现频谱数据预测；最后计算预测数据与真实数据的均方误差，判别频谱异常.该算法能在无监督学习的条件下，实现多种类异常状态检测的.在公开频谱数据的多个频段对算法性能进行验证，结果表明本文算法能够有效地实现电磁频谱异常检测.

Abstract

To solve the problem of low efficiency of electromagnetic spectrum anomaly detection

we propose a new method of spectrum anomaly detection based on depth feature fusion which combines convolutional neural networks(CNN) and long short-term memory(LSTM) Networks. Firstly

a deep feature extraction network is constructed

which includes a multi-level CNN and a LSTM. The network can extract depth features in a hierarchical manner. Then

pooling layer

concatenate layer and other operations are used to fuse the depth features to achieve high-precision prediction of spectrum data. Finally

the mean square error between the predicted data and the real data is calculated by discriminating the spectrum anomaly. The algorithm can detect multiple kinds of abnormal states under the condition of unsupervised learning. We verified our algorithm in frequency bands of public spectrum data. The results show that our algorithm can effectively realize electromagnetic spectrum anomaly detection.

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