Automatic modulation classification of radar signals utilizing X-net

Automatic modulation classification (AMC) of radar signals has long been a challenge, especially in the area of electronic reconnaissance, where collecting and labeling numerous signal samples are usually harsh and impracticable. In this article, a novel recognition network is proposed for detecting...

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Bibliographic Details
Published in:Digital signal processing Vol. 123; p. 103396
Main Authors: Chen, Kuiyu, Zhang, Jingyi, Chen, Si, Zhang, Shuning, Zhao, Huichang
Format: Journal Article
Language:English
Published: Elsevier Inc 30.04.2022
Subjects:
Automatic modulation classification
Measured data
Noise level estimation
Radar signals
Residual learning convolutional denoising autoencoder
Supplementary classification networks
Radar signals
Measured data
Residual learning convolutional denoising autoencoder
Supplementary classification networks
Noise level estimation
Automatic modulation classification
ISSN:1051-2004
Online Access:Get full text
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Summary:Automatic modulation classification (AMC) of radar signals has long been a challenge, especially in the area of electronic reconnaissance, where collecting and labeling numerous signal samples are usually harsh and impracticable. In this article, a novel recognition network is proposed for detecting radiation signals under intense noise background with only simulation samples for model training. Owing to the X-shaped structure, the recognition network is named X-net. A residual learning convolutional denoising autoencoder (RLCDAE) and a supplementary classification network based on noise level estimation (NLE) constitute the X-net. Via pre-training of RLCDAE, the robustness against noise is enhanced. Then, a supplementary classification network further improves the recognition performance under low signal-to-noise ratios (SNRs). To evaluate the method, comparative experiments with some excellent algorithms on noise immunity and recognition accuracy are conducted. The cognitive system can still recognize ten kinds of radar signals with an overall precision of 96% even when the SNR is −8 dB. Furthermore, simulation samples trained model is verified by measured data. Outstanding performance proves the effectiveness and superiority of the proposed method on cognitive radar signals under low SNRs.
ISSN:1051-2004
DOI:10.1016/j.dsp.2022.103396