Neural network-assisted signal processing in Brillouin optical correlation-domain sensing for potential high-speed implementation

The general neural networks (NNs) based on classification convert the Brillouin frequency shift (BFS) extraction in Brillouin-based distributed sensing to a problem in which the possible BFS output of the sensing system belongs to a finite number of discrete values. In this paper, we demonstrate a method of applying NNs with adaptive BFS incremental steps to signal processing for Brillouin optical correlation-domain sensing and achieve higher accuracy and operation speed. The comparison with the conventional curving fitting method shows that the NN improves the BFS measurement accuracy by 2-3 times and accelerates the signal processing speed by 1000 times for simulated signals. The experimental results demonstrate the NN provides 1.6-2.7 times enhancement for BFS measurement accuracy and 5000 times acceleration for the BFS extraction speed. This method supplies a potential solution to online signal processing for real-time Brillouin sensing.