Research On Performance Improvement Of Conventional Brillouin Sensing System Based On Compressed Sensing

Distributed optical fiber sensors(DOFSs)are able to measure a spatially distributed profile of environmental quantities such as temperature,strain,pressure,etc.,and this distributed-measurement capability offers unique advantage compared to conventional discrete sensing techniques,especially for their capability of long-distance measurement with a single unaltered optical fiber as the sensing element.Time-domain based Brillouin sensing technique,such as Brillouin optical time-domain analysis(BOTDA)or Brillouin optical time-domain reflectometry(BOTDR),has been proven to be one of the most popular distributed-sensing techniques that are able to measure temperature and strain over many tens of kilometers with moderate spatial resolution.In order to obtain high signal-to-noise ratio(SNR)Brillouin spectra,both the conventional BOTDA and BOTDR need to scan over a wide frequency range discretely around the Brillouin frequency of the optical fiber,so that a large amount of frequencies need to measured.At each frequency component,a large number of time-domain traces need to be averaged in order to achieve decent SNR since the backscattering signal is rather weak.Hence,it takes considerable time to complete one measurement over rather long distance using Brillouin sensing system.In this thesis,we propose to adopt compressive sampling(CS)principle to enhance the performance of Brillouin sensing systems.CS theory asserts that certain signals can be recovered from far fewer samples or measurements than the conventional methods use.CS reconstructs signals from significantly fewer measurements than traditionally required;thus it has the potential to reconstruct Brillouin spectra at different locations along an optical fiber from much fewer number of measurement of the frequency components.We use a fast iterative shrinkage-thresholding algorithm(FISTA)for the Brillouin spectra recovery,realizing significant signal-to-noise ratio enhancement and dramatic data storage reduction at the same time.By compressing and storing a small amount of data,the original spectrum can be reconstructed and at the same time the signal-to-noise can be improved dramatically,so that the pressure on system data storage is reduced.Our ultimate goal is to measure much fewer frequency samples to achieve spectral reconstruction and noise reduction,which will greatly improve the practicality of the time-domain Brillouin sensing technique.This thesis uses BOTDA sensing system for demonstration purpose,and the main contents are organized as follows:Firstly,we introduce the research status of optical fiber sensing technology and analyze the distributed optical fiber sensing system based on Brillouin scattering.Then,we introduce BOTDA principle and the BOTDA systems,explain the basic principle of compressive sampling,and introduce fast iterative shrinkage-thresholding algorithm.Finally,we conduct simulations to verify the feasibility to reconstruct the Brillouin spectrum based on CS.We use fast iterative shrinkage-thresholding algorithm to perform reconstruction of Lorentz functions,give the reconstruction criteria to determine whether the recovery is successful.We study the relationship between compression ratio and the reconstruction successful rate.The signal-to-noise improvement with different noise levels are also investigated.When the compression rate is about 30%,one can successfully reconstruct the Lorentz function and at the same time reducing noises greatly.Then,we use compressive sampling to reconstruct the spectral data of BOTDA sensing system,which can also achieve spectral reconstruction and noise reduction with 30% of the original spectral data.In this thesis,we investigate the feasibility to use CS principle for spectral recovery of the conventional time-domain Brillouin sensing systems.Sensing performance can be enhanced without expensive hardware modification to reduce the data storage and improve the signal-tonoise ratio at the same time.