Real-time Fabry-Pérot cavity length demodulation via deep learning-based compressed sensing

Fiber-optic Fabry-Pérot sensors have already been applied to many applications that require fast dynamic measurement due to their superior advantages of electromagnetic interference immunity, high resolution, fast response time, etc. In order to maintain high measurement accuracy, a fast demodulation technique based on white light interferometry is essential for accurate Fabry-Pérot cavity length determination. In this work, we utilize a deep learning network incorporating a compressed sensing algorithm, achieving real-time cavity length demodulation using a Vernier-tuned distributed feedback laser whose output wavelength can be rapidly tuned within a very broad spectral bandwidth. Our method can, in principle, achieve a measurement sampling rate equivalent to the laser wavelength switching rate, up to 10 MHz, over 2 orders of magnitude enhancement compared to the existing techniques, which clearly has great potential in applications that require high-resolution and high-speed absolute measurement.