Abstract
A deep-learning-based time–frequency domain signal recovery method is proposed to deal with the signal distortion in fiber-connected radar networks. In this method, the deteriorated signal is converted to the time–frequency domain, and a two-dimensional convolutional neural network is used to conduct signal recovery before inverse conversion to the time domain. This method can achieve high-accuracy signal recovery by learning the complete features in both time and frequency domains. In the experiment, distorted linear frequency modulated radar signals with a bandwidth of 2 GHz after 8-km fiber transmission are recovered with the noise effectively suppressed. The proposed signal recovery method works well under different input signal-to-noise ratios. Specially, the average peak to floor ratio after radar pulse compression is improved by 25.5 dB. In addition, the method is proved to be able to recover radar signals of multiple targets.
© 2021 Optica Publishing Group
Full Article | PDF ArticleMore Like This
Shiyang Liu, Shi Wang, Taixia Shi, and Yang Chen
Opt. Lett. 48(3) 767-770 (2023)
Shaofu Xu, Rui Wang, Xiuting Zou, and Weiwen Zou
J. Opt. Soc. Am. B 38(3) 834-841 (2021)
Sicheng Yi, Shaofu Xu, and Weiwen Zou
Opt. Lett. 46(23) 5982-5985 (2021)