Research Article
Generative Adversarial Network for Generating Time-Frequency Images
@INPROCEEDINGS{10.1007/978-3-030-69066-3_9, author={Weigang Zhu and Kun Li and Wei Qu and Bakun Zhu and Hongyu Zhao}, title={Generative Adversarial Network for Generating Time-Frequency Images}, proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings}, proceedings_a={AICON}, year={2021}, month={7}, keywords={Radar emitter identification Time-frequency image GAN Sample diversity SVD}, doi={10.1007/978-3-030-69066-3_9} }- Weigang Zhu
Kun Li
Wei Qu
Bakun Zhu
Hongyu Zhao
Year: 2021
Generative Adversarial Network for Generating Time-Frequency Images
AICON
Springer
DOI: 10.1007/978-3-030-69066-3_9
Abstract
To deal with the problem of de-noising and enhancement of radar signal time-frequency images, a method of secondary generating time-frequency images by generative adversarial network is proposed. Firstly, time-frequency analysis is used to generate the time-frequency image of the radar signal as the original data set 1. Then, after learning the data set 1 by using the generative adversarial network, a new data set 2 is generated, and the data set 2 has de-noising and enhancement effects relative to data set 1. Finally, the validity of the data set 2 generated by the time-frequency image singular value feature is checked. Experiments on the time-frequency images of five common radar signals are carried out. The results show that the method is effective in time-frequency image de-noising and increasing sample diversity.