Training-Free Feature Extraction of BOTDA Based on Sparse Representation

We propose a method based on sparse representation to extract amplitude, linewidth, and Brillouin frequency shift (BFS) in BOTDA using dictionary-learning algorithm without feedback and off-line training, which enables more accurate BFS measurements in real-time.