LPI Radar Signal Enhancement Based on Generative Adversarial Networks under Small Samples

Wangkui Jiang, Yan Li*, Zhen Tian

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

With the widespread deployment of low probability of intercept (LPI) radar systems, signal processing of LPI waveforms is becoming a key technology in the modern electronics field. In this paper, a signal enhancement framework aimed at denoising and restoring noisy time-frequency images (TFIs) of LPI radar signals is proposed. The method applies generative adversarial networks (GANs) to this field and conducts training in the case of small samples. A reasonable loss function is designed to optimize the model of signal enhancement at the same time. Furthermore, we utilize several classifiers to prove the validity of the model. Simulation results on eight kinds of typical radar signals demonstrate that the noisy TFIs can be well recovered. And the subsequent classification accuracy is greatly improved by using plain convolutional neural network (CNN), residual network (Resnet), visual geometry group (VGG) network, or any other method.

源语言英语
主期刊名2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020
出版商Institute of Electrical and Electronics Engineers Inc.
1171-1175
页数5
ISBN(电子版)9781728186351
DOI
出版状态已出版 - 11 12月 2020
活动6th IEEE International Conference on Computer and Communications, ICCC 2020 - Chengdu, 中国
期限: 11 12月 202014 12月 2020

出版系列

姓名2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020

会议

会议6th IEEE International Conference on Computer and Communications, ICCC 2020
国家/地区中国
Chengdu
时期11/12/2014/12/20

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