Self-Mixing Interferometric Signal Enhancement Using Generative Adversarial Network for Laser Metric Sensing Applications

Measurement performance of self-mixing interferometric (SMI) laser sensor can be significantly affected due to the presence of noise. In this case, conventional signal enhancement techniques yield compromised performance due to several limitations which include processing signals in frequency domains only, relying mainly on first order statistics, loss of important information present in higher frequency band and handling limited number of noise types. To address these issues, we propose a solution based on using generative adversarial network, a popular deep learning scheme, to enhance SMI signal corrupted with different noise types. Thus, taking advantage of the deep networks that can learn arbitrary noise distribution from large example set, our proposed method trains the deep network model end-to-end, able to process raw waveforms directly, learn 51 different noise conditions including white noise and amplitude modulation noise for 1,140 different types of SMI waveforms made up of 285 different optical feedback coupling factor (
$C$
) values and 4 different line-width enhancement factor
$\alpha $
values. The results show that the proposed method is able to significantly improve the SNR of noisy SM signals on average of 19.49, 16.29, 10.34 dB for weak-, moderate-, and strong-optical feedback regime signals, respectively. For amplitude modulated SMI signals, the proposed method has corrected the amplitude modulation with maximum error (using area-under-the-curve based quantitative analysis) of 0.73% for SMI signals belonging to all optical feedback regimes. Thus, our proposed method can effectively reduce the noise without distorting the original signal. We believe that such a unified and precise method leads to enhancement of performance of SMI laser sensors operating under real-world, noisy conditions.