TY - GEN
T1 - LPI Radar Signal Enhancement Based on Generative Adversarial Networks under Small Samples
AU - Jiang, Wangkui
AU - Li, Yan
AU - Tian, Zhen
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/11
Y1 - 2020/12/11
N2 - 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.
AB - 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.
KW - generative adversarial networks
KW - signal enhancement
KW - time-frequency images
UR - http://www.scopus.com/inward/record.url?scp=85101702536&partnerID=8YFLogxK
U2 - 10.1109/ICCC51575.2020.9345130
DO - 10.1109/ICCC51575.2020.9345130
M3 - Conference contribution
AN - SCOPUS:85101702536
T3 - 2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020
SP - 1171
EP - 1175
BT - 2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Computer and Communications, ICCC 2020
Y2 - 11 December 2020 through 14 December 2020
ER -