Advancement in fast measurement and processing techniques of distributed Brillouin optical time-domain analysis fiber sensors: Methods, challenges, and future directions

Significant advancements have been made in distributed Brillouin optical time-domain analysis (BOTDA) fiber sensing, with a particular focus on rapid measurement techniques. This review comprehensively discusses the latest methods for measuring temperature and strain in dynamic environments subject to vibrations and pressure. We first examine the fundamental principles of BOTDA fiber sensors and evaluate the speed limitations of conventional systems. The paper then analyzes and summarizes modern hardware and software solutions that address the time-consuming nature of traditional frequency scanning. Specifically, we detail advancements in fast frequency scanning, signal detection, and data processing, including the application of machine learning algorithms. The paper also discusses the current challenges and future directions for high-speed BOTDA technology, which are critical for developing more powerful, flexible, and efficient applications. Ultimately, this review serves as a valuable resource for researchers aiming to improve rapid measurement techniques in distributed Brillouin optical time-domain analysis fiber sensors.

Abbreviations: AI, Artificial Intelligence; ANN, Artificial Neural Network; AWG, Arbitrary Waveform Generator; BFS, Brillouin Frequency Shift; BGS, Brillouin Gain Spectrum; BOCDA, Brillouin Optical Correlation-Domain Analysis; BOCDR, Brillouin Optical Correlation-Domain Reflectometry; BOTDA, Brillouin Optical Time-Domain Analysis; BOTDR, Brillouin Optical Time-Domain Reflectometry; BP, Back Propagation; BPS, Brillouin Phase Spectrum; CAZAC, Constant Amplitude Zero Auto Correlation; CNN, Convolutional Neural Network; CPCA, Complex Principal Component Analysis; CW, Continuous Wave; DAE, Denoising Autoencoder; DAQ, Data Acquisition; DBOFS, Distributed Brillouin Optical Fiber Sensors; DL, Deep Learning; DNN, Deep Neural Network; DOFS, Distributed Optical Fiber Sensors; DPP, Differential Pulse-Pair; DSS, Distributed Strain Sensing; DTS, Distributed Temperature Sensing; DTSS, Distributed Temperature and Strain Sensing; ELM, Extreme Learning Machine; EOM, Electro-Optic Modulator; ESN, Echo State Network; F-BOTDA, Fast BOTDA; FNN, Feedforward Neural Network; FUT, Fiber Under Test; GAN, Generative Adversarial Network; GLM, Generalized Linear Model; IM-DD, Intensity Modulation-Direct Detection; IQ, In-Phase Quadrature; K-ELM, Kernel Extreme Learning Machine; K-SVD, K-Singular Value Decomposition; KNN, K-Nearest Neighbour; LCF, Lorentz Curve Fitting; LEAF, Large Effective Area Fiber; LSTM, Long Short-Term Memory; MAE, Mean Absolute Error; ML, Machine Learning; OCC, Optical Chirp Chain; OFA, Optical Frequency-Agile; OFC, Optical Frequency Comb; OFDM, Orthogonal Frequency Division Multiplexing; PAPR, Peak-to-Average Power Ratio; PC, Polarization Controller; PCA, Principal Component Analysis; K-ELM, Kernel Extreme Learning Machine; K-SVD, K-Singular Value Decomposition; KNN, K-Nearest Neighbour; LCF, Lorentz Curve Fitting; LEAF, Large Effective Area Fiber; LSTM, Long Short-Term Memory; MAE, Mean Absolute Error; ML, Machine Learning; OCC, Optical Chirp Chain; OFA, Optical Frequency-Agile; OFC, Optical Frequency Comb; OFDM, Orthogonal Frequency Division Multiplexing; PAPR, Peak-to-Average Power Ratio; PC, Polarization Controller; PCA, Principal Component Analysis; PCA-PDNN, PCA-Trained Probabilistic Deep Neural ; , Network; PD, Photodetector; PID, Proportional-Integral-Derivative; PT, Peak-Tracking; RL, Reinforcement Learning; RNN, Recurrent Neural Network; SA, Slope-Assisted; SA-BOTDA, Slope-Assisted BOTDA; SBS, Stimulated Brillouin Scattering; SDNet, Self-Denoising Network; SMF, Single-Mode Fiber; SNR, Signal-to-Noise Ratio; SS-BOTDA, Slope-Slope BOTDA; SVM, Support Vector Machine; SVR, Support Vector Regression.