Enhancement of frequency scanning interferometry signal for non-cooperative target based on generative adversarial network

In non-cooperative target frequency scanning interferometry, the return optical power is low, the quality of the interferometric signal is poor, and the signal-to-noise ratio (SNR) is low. Moreover, the power change accompanying the use of the frequency scanning laser modulates the interferometric signal's amplitude and shifts the amplitude centre. Traditional signal enhancement techniques, such as filtering, can only solve some of the problems affecting the measurement accuracy, and the full-factor processing of such signals is difficult. This paper proposes a non-cooperative target frequency scanning interferometry signal enhancement method based on a generative adversarial network. By learning the sample dataset, the SNR of the signal can be improved within a certain range, and the signal waveform can be corrected simultaneously. The simulation results reveal that the SNR of the non-cooperative target signal is improved and the signal waveform is satisfactorily corrected. Finally, the effectiveness of the enhancement method was experimentally confirmed.