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Training-free feature extraction of BOTDA based on sparse representation

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Abstract

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.

© 2020 The Author(s)

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