Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning.
Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning.
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基于自适应奇异值重构和深度残差学习的雷达信号调制识别
DOI：
10.3390/s21020449
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发表时间：
2021-01-10
期刊：
影响因子：
--
通讯作者：
Zhao H
中科院分区：
文献类型：
--
作者：
Chen K;Zhang S;Zhu L;Chen S;Zhao H
雷达信号调制方式的自动识别是电子情报系统中必不可少的生存技术。为了避免复杂的特征提取过程，实现低信噪比下多种雷达信号调制方式的智能识别，提出了一种基于雷达信号脉内特征的自适应奇异值重构（ASVR）和深度残差学习方法。首先对低信噪比下的雷达信号进行ASVR去噪处理，改善信号的时频谱;其次，采用二值化、形态学滤波等一系列图像处理技术对时频分布图像进行背景噪声抑制。第三，利用TFDIs实现了残差网络的训练过程，并利用新训练好的网络实现了各种条件下的分类。仿真结果表明，对于8种调制信号，当信噪比仅为-8 dB时，该方法仍然达到了94.1%的总成功识别概率。出色的性能证明了所提出的方法的优越性和鲁棒性。
Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.
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影响因子：
19.5
作者：
Russakovsky, Olga;Deng, Jia;Fei-Fei, Li
通讯作者：
Fei-Fei, Li
影响因子：
3.6
作者：
Bezousek, P;Schejbal, V
通讯作者：
Schejbal, V
改进了缺失数据时间序列的奇异谱分析
影响因子：
2.2
作者：
Shen, Y.;Peng, F.;Li, B.
通讯作者：
Li, B.
DOI：
10.1109/tpami.1987.4767941
发表时间：
1987-07-01
影响因子：
23.6
作者：
HARALICK, RM;STERNBERG, SR;ZHUANG, XH
通讯作者：
ZHUANG, XH
DOI：
10.1109/lcomm.2018.2809732
发表时间：
2018-05-01
期刊：
IEEE COMMUNICATIONS LETTERS
影响因子：
--
作者：
Li, Rundong;Li, Lizhong;Li, Shaoqian
通讯作者：
Li, Shaoqian