Radar Emitter Signal Recognition Based on Dilated Residual Network

QIN Xin; HUANG Jie; ZHA Xiong; LUO Li-ping; HU De-xiu

doi:10.3969/j.issn.0372-2112.2020.03.006

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Radar Emitter Signal Recognition Based on Dilated Residual Network

更新时间：2025-07-16

- Radar Emitter Signal Recognition Based on Dilated Residual Network
- Acta Electronica Sinica Vol. 48, Issue 3, Pages: 456-462(2020)
- 作者机构：
  
  中国人民解放军战略支援部队信息工程大学,河南,郑州,450001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61703433)
- DOI：10.3969/j.issn.0372-2112.2020.03.006
  CLC： TN971
- Published Online：25 March 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
QIN Xin, HUANG Jie, ZHA Xiong, et al. Radar Emitter Signal Recognition Based on Dilated Residual Network[J]. Acta Electronica Sinica, 2020, 48(3): 456-462.
DOI：

QIN Xin, HUANG Jie, ZHA Xiong, et al. Radar Emitter Signal Recognition Based on Dilated Residual Network[J]. Acta Electronica Sinica, 2020, 48(3): 456-462. DOI： 10.3969/j.issn.0372-2112.2020.03.006.

摘要

针对低信噪比条件下，复杂多类雷达辐射源信号识别存在特征提取困难，识别正确率低的问题，本文提出了一种基于时频分析和扩张残差网络的辐射源信号自动识别方法.首先通过时频分析将信号时域波形转换成二维时频图像以反映信号本质特征；然后进行时频图像预处理以保留时频图像完备信息，适应深度学习模型输入；最后构建扩张残差网络以自动提取信号时频图像特征，实现雷达辐射源信号分类识别.实验结果表明，信噪比为-6dB时，该方法对16类雷达辐射源信号的整体识别正确率能够达到98.2%，对时频图像特征相似的类LFM（Linear Frequency Modulation）信号的整体识别正确率超过95%.本文提供了一种新的雷达辐射源信号智能识别方法，具有较好的工程应用前景.

Abstract

This paper proposes a radar emitter signal recognition method based on time-frequency analysis and dilated residual network (DRN) to solve the problem of difficulty in feature extraction and low accuracy in recognition of complex multiple radar emitter signals under low signal-to-noise ratio (SNR). Firstly

the signal time-domain waveform is transformed into a two-dimensional time-frequency image by time-frequency analysis to reflect the essential characteristics of signal. Then the time-frequency image pre-processing is carried out to retain the time-frequency image complete information and adapt to the deep learning model input. Finally

the DRN is constructed to automatically extract the signal time-frequency image features and realize the recognition of radar emitter signal. Experimental results show that when the SNR is -6dB

the overall recognition rate of the proposed method for 16 types of radar signals can reach 98.2%

and the overall recognition rate for time-frequency image similar to linear frequency modulation (LFM) signals is more than 95%. In this paper

a new intelligent recognition method for radar emitter signal is presented

which has nice engineering application prospects.

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