Investigation Of Improving BOTDA Measurement Accuracy Based On EMD-FIR Denoising Algorithm And PSO-XCM BFS Extraction Algorithm

Brillouin Optical Time Domain Analysis(BOTDA)is a distributed optical fiber sensing technology based on Brillouin scattered,which can be used to monitor the strain and temperature changes along the sensing fiber.This sensing technology has the advantages of multi-parameter measurement and long sensing distance,so it is widely used in large-scale buildings,smart grids,oil and gas pipelines and other fields to achieve the purpose of sensing monitoring.However,if the sensing monitoring with high measurement accuracy cannot be achieved,it will cause unpredictable losses in some fields(such as aerospace).Therefore,achieving high measurement accuracy is an urgent problem for the BOTDA system.In view of the above problem,a new denoising algorithm and a Brillouin frequency shift(BFS)extraction algorithm are proposed in this paper to improve the measurement accuracy of BOTDA system.The main research work is as follows:(1)A new denoising algorithm based on empirical mode decomposition(EMD)and finite impulse response(FIR)filtering is proposed to effectively improve the signal-to-noise ratio(SNR),thereby improving the sensing measurement accuracy of BOTDA system.First,the denoising algorithm is applied to Brillouin signal along fiber of BOTDA sensing system under the condition of multiple pump pulse widths.The results show that for a 6 km sensing fiber with a minimum spatial resolution of 3 m,EMD-FIR denoising algorithm improves SNR by 11.69 d B,which is 4.98 d B and 4.26 d B higher than wavelet denoising and Butterworth denoising.Subsequently,the algorithm is used to process the BOTDA Brillouin signal along fiber containing the heating zone.The denoising results show that SNR after processing by EMD-FIR is improved by 9.83 d B.This value is 4.13 d B and 3.94 d B higher than wavelet and Butterworth denoising,and it can be judged that EMD-FIR algorithm will not deteriorate the spatial resolution by the rising edge of timing signal.Finally,the EMD-FIR algorithm is used to process the whole data sets of Brillouin gain spectrum(BGS)distribution containing heated section.The temperature measurement error before noise reduction is 3.09°C,and that after noise reduction is 0.93°C.It can be known that the temperature measurement accuracy is increased by 2.16°C compared with that without noise reduction,and the temperature uncertainty is reduced to 0.62°C,which greatly reduces the system error and improves the measurement accuracy.(2)A BFS extraction algorithm based on particle swarm optimization(PSO)and cross-correlation method(XCM)is proposed,which effectively reduces measurement error and achieves high measurement accuracy and high fitting degree sensing monitoring.Firstly,the extraction algorithm is used to process simulated BGS data to verify the feasibility and explore its related characteristics.The simulation data contain BGS signals in various situations,including different SNRs,different bandwidths,different sweep intervals,and different symmetry.The processing results show that compared with the traditional Lorentz fitting algorithm and XCM algorithm,the optimal BFS extraction error of the extraction algorithm is 99.98% and 99.93% respectively and the maximum fitting optimization is 98%.Then use the PSO-XCM extraction algorithm to process the experimental BGS data with a sensing distance of 10 km,a heating zone length of 100 m,and a spatial resolution of 10 m to verify the actual feasibility and explore its actual measurement error.From the results,it can be seen that the minimum temperature measurement error can reach 0.06? and measured temperature coefficient is 1.12 MHz/?,that is,the minimum BFS measurement error is 0.07 MHz.It can be concluded that the PSO-XCM algorithm not only has practical feasibility,but also achieves high-precision temperature measurement.