This review describes state-of-the-art software and hardware methods for detecting the frequency shift of a stimulated and spontaneous Brillouin scattering spectrum for the needs of the distributed fiber-optic sensors and fiber-optic metrology industry. Current approaches to optical-fiber probing and signal detection are presented. Digital filtering methods that provide an increase in the signal-to-noise ratio of the spectrum are described. A review of Lorentzian curve fitting (LCF) spectrum approximation methods, correlation methods, artificial intelligence methods, and machine learning methods has been made.

In this paper, we studied the possibility of increasing the Brillouin frequency shift (BFS) detection accuracy in distributed fibre-optic sensors by the separate and joint use of different algorithms for finding the spectral maximum: Lorentzian curve fitting (LCF, including the Levenberg–Marquardt (LM) method), the backward correlation technique (BWC) and a machine learning algorithm, the generalized linear model (GLM). The study was carried out on real spectra subjected to the subsequent addition of extreme digital noise. The precision and accuracy of the LM and BWC methods were studied by varying the signal-to-noise ratios (SNRs) and by incorporating the GLM method into the processing steps. It was found that the use of methods in sequence gives a gain in the accuracy of determining the sensor temperature from tenths to several degrees Celsius (or MHz in BFS scale), which is manifested for signal-to-noise ratios within 0 to 20 dB. We have found out that the double processing (BWC + GLM) is more effective for positive SNR values (in dB): it gives a gain in BFS measurement precision near 0.4 °C (428 kHz or 9.3 $\mathsf{\mu }\mathsf{\epsilon }$); for BWC + GLM, the difference of precisions between single and double processing for SNRs below 2.6 dB is about 1.5 °C (1.6 MHz or 35 $\mathsf{\mu }\mathsf{\epsilon }$). In this case, double processing is more effective for all SNRs. The described technique’s potential application in structural health monitoring (SHM) of concrete objects and different areas in metrology and sensing were also discussed.

A new method of Brillouin spectra post-processing, which could be applied in modern distributed optical sensors: Brillouin optical time domain analyzers/reflectometers (BOTDA/BOTDR), has been demonstrated. It operates by means of the correlation analysis performed with special technique («backward-correlation»). It does not need any additional data for time or space averaging and operates with the single spectrum only. We have simulated the method accuracy dependence on signal-to-noise ratio (SNR) and other parameters. It is shown that the new method produces better results at low SNRs than conventional technique, based on finding of Brillouin spectrum maximum, do. These results are in a good agreement with the experiment. Finally, we have estimated the performance of the new method for its application in polarization-BOTDA set-up for a polarization maintaining (PM) fiber modal birefringence distributed study.

Using the same extremely noised data, two correlation methods for finding the maxima of Brillouin spectra are compared. The first method is a well-known method of correlating the received signal with the ideal Lorentzian function. In the second method, developed by the authors earlier, instead of the Lorentzian function, the same spectrum under study, but inverted along the frequency axis, is used (Backward correlation method, BWC). In addition to evaluating the accuracy of both methods, they are compared with the classical method of Lorentzian curve fitting. The accuracy of the considered methods is estimated depending on the probability of artefacts appearing in the Brillouin scattering spectra. It is shown that when the 9% probability of the artifact occurrence is exceeded, the BWC method shows better results than the other methods considered.

This paper demonstrates how the processing of Brillouin gain spectra (BGS) by two-dimensional correlation methods improves the accuracy of Brillouin frequency shift (BFS) extraction in distributed fiber optic sensor systems based on the BOTDA/BOTDR (Brillouin optical time domain analysis/reflectometry) principles. First, the spectra corresponding to different spatial coordinates of the fiber sensor are resampled. Subsequently, the resampled spectra are aligned by the position of the maximum by shifting in frequency relative to each other. The spectra aligned by the position of the maximum are then averaged, which effectively increases the signal-to-noise ratio (SNR). Finally, the Lorentzian curve fitting (LCF) method is applied to the spectrum with improved characteristics, including a reduced scanning step and an increased SNR. Simulations and experiments have demonstrated that the method is particularly efficacious when the signal-to-noise ratio does not exceed 8 dB and the frequency scanning step is coarser than 4 MHz. This is particularly relevant when designing high-speed sensors, as well as when using non-standard laser sources, such as a self-scanning frequency laser, for distributed fiber-optic sensing.

A new method for extracting the Brillouin frequency shift (BFS) from the Brillouin gain spectrum (BGS), the modified backward correlation method (MBWC), is presented. The possibilities of using MBWC, and MBWC in combination with the Lorentzian curve fitting (LCF) based on Levenberg–Marquardt (LM) method, are studied. The effectiveness of the new method, and its combination with LM, has been demonstrated for processing spectra with a low signal-to-noise ratio (SNR). The experiments, which were in good agreement with the performed simulation, showed that at SNR = 0 dB, the combined (MBWC + LM) method provided the BFS extraction error of less than 4 MHz, while the state-of-the-art LM algorithm extracted it with the error greater than 4.5 MHz. The advantage of correlation methods becomes more significant with the decreasing SNR: at SNR = −2 dB, the LM’s error is 14.3 MHz, and that of the combined one is 8.1 MHz.