Journal of Xidian University ›› 2023, Vol. 50 ›› Issue (6): 133-147.doi: 10.19665/j.issn1001-2400.20230312

• Information and Communications Engineering & Computer Science and Technology • Previous Articles     Next Articles

Generative adversarial model for radar intra-pulse signal denoising and recognition

DU Mingyang(),DU Meng(),PAN Jifei(),BI Daping()   

  1. College of Electronic Engineering,National University of Defense Technology,Hefei 230037,China
  • Received:2023-01-03 Online:2023-12-20 Published:2024-01-22


While deep neural networks have achieved an impressive success in computer vision,the related research remains embryonic in radio frequency signal processing,i.e.,a vital task in modern wireless systems,for example,the electronic reconnaissance system.Noise corruption is a harmful but unavoidable factor causing severe performance degradation in the signal processing procedure,and thus has persistently been an intractable problem in the radio frequency domain.For example,a classifier trained on the high signal-to-noise ratio(SNR) data might experience a severe performance degradation when dealing with low SNR data.To address this problem,in this paper we leverage the powerful data representation capacity of deep learning and propose a Generative Adversarial Denoising and classification Network(GADNet) for radar signal restoration and a classification task.The proposed GADNet consists of a generator,a discriminator and a classifier fulfilling an end-to-end workflow.The encoder-decoder structure generator is trained to extract the high-level features and recover signals.Meanwhile,it fools the discriminator’s judges by bewildering the denoising results coming from the clean data.The classification loss from the classifier is adopted jointly to the training procedure.Extensive experiments demonstrate the benefit of the proposed technique in terms of high-quality restoration and accurate classification for radar signals with intense noise.Moreover,it also exhibits superior transferability in low SNR environments compared to the state-of-the-art methods.

Key words: radar emitter, signal recognition, convolutional neural networks, generative adversarial network, signal denoising

CLC Number: 

  • TN971