Performance enhancement of Brillouin sensing systems based on compressive sampling

Abstract

Compressive sampling theory asserts that certain signals can be recovered from far fewer samples than traditional methods use. We propose to enhance the performance of Brillouin sensing systems by improving the signal-to-noise ratio of the Brillouin spectra with random undersampled measurements of the original noisy Brillouin spectra. The number of acquisitions can be significantly reduced, and at the same time the measurement accuracy can be improved due to the increased signal-to-noise ratio of recovered Brillouin spectra measured based on compressive sampling principle compared to those measured directly by conventional methods. Experiments show that by performing ∼30% of the acquisitions that are required by conventional systems, over 7 dB signal-to-noise ratio enhancement can be obtained. Our proposal can be applied to any practical Brillouin sensing system whose performance can be enhanced by taking the advantages of recent advancements in computational methods without costly or sophisticated hardware modifications.