基于卷积神经网络的雷达辐射源识

doi:10.3969/j.issn.1009-086x.2021.03.017

现代防御技术 ›› 2021, Vol. 49 ›› Issue (3): 130-136.DOI: 10.3969/j.issn.1009-086x.2021.03.017

• 仿真技术 • 上一篇

基于卷积神经网络的雷达辐射源识

牛浩楠¹, 王文灿², 刘清波²

1.北京无线电测量研究所,北京 100854;
2.中国人民解放军96901部队,北京 100094

收稿日期:2020-12-23 修回日期:2021-03-06 出版日期:2021-06-20 发布日期:2021-07-12
通讯作者: 100854 北京142号信箱203分箱26号 E-mail:nmkyonly@163.com
作者简介:牛浩楠(1995-),女,山西汾阳人。工程师,硕士,主要从事雷达抗干扰研究。

Radar Radiation Source Identification Based on Convolution Neural Network

NIU Hao-nan¹, WANG Wen-can², LIU Qing-bo²

1. Beijing Institute of Radio Measurement,Beijing 100854,China;
2. PLA No.96901 Troop,Beijing 100094,China

Received:2020-12-23 Revised:2021-03-06 Online:2021-06-20 Published:2021-07-12

摘要/Abstract

摘要： 随着电磁环境越来越复杂多变,给电子对抗带来了很大的挑战,基于传统的脉冲描述字和信号识别方法已不能满足战场要求。针对复杂电磁环境下的辐射源精确识别问题,将基于相控阵雷达辐射源的细微特征,主要是包络特征和相噪特征,对其进行分析建模,并结合卷积神经网络提出了一种基于雷达中频数据和一维卷积神经网络的雷达辐射源个体识别方法,训练一维卷积神经网络来学习雷达辐射源信号的有效特征并进行识别。最后在不同信噪比条件下进行了识别仿真实验,得到了较好的识别正确率,证明了该方法的有效性和可行性。

关键词: 辐射源识别, 相控阵雷达, 细微特征, 中频数据, 一维卷积神经网络, 信噪比

Abstract: As the electromagnetic environment becomes more and more complex and changeable,it brings great challenges to electronic countermeasures.The traditional pulse description and signal recognition methods cannot meet the requirements of the battlefield.Aiming at the problem of accurate identification of radiation sources in complex electromagnetic environments,the phased array radar emitter is moded based on its microscopic features such as envelope and phase noise characteristics.A method for individual identification of radar radiation sources based on radar IF data and one-dimensional convolutional neural networks is proposed in combination with convolutional neural networks.A one-dimensional convolutional neural network is trained to learn and identify the effective features of radar radiation source signals.The recognition simulation experiments are conducted under different signal-to-noise ratio conditions with high recognition correct rates,which proves the effectiveness and feasibility of the method.

Key words: source identification, phased array radar, refined features, intermediate-frequency data, one-dimensional convolutional neural network, signal to noise ratio

中图分类号:

TN95
TJ76

牛浩楠, 王文灿, 刘清波. 基于卷积神经网络的雷达辐射源识[J]. 现代防御技术, 2021, 49(3): 130-136.

NIU Hao-nan, WANG Wen-can, LIU Qing-bo. Radar Radiation Source Identification Based on Convolution Neural Network[J]. Modern Defense Technology, 2021, 49(3): 130-136.

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基于卷积神经网络的雷达辐射源识

Radar Radiation Source Identification Based on Convolution Neural Network

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