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Deep-learning-based time–frequency domain signal recovery for fiber-connected radar networks

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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.

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Data underlying the results presented in this Letter are not publicly available at this time but may be obtained from the authors upon reasonable request.

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