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Robust One-Dimensional Phase Unwrapping Algorithm Based on LSTM Network With Reduced Parameter Number
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DOI：10.1109/JLT.2022.3195932.png)
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Abstract
En 中文
For interferometric optical fiber sensor applications, one-dimensional (1D) phase unwrapping is one of the most intractable steps. It is generally more vulnerable to noise impact compared with two-dimensional (2D) phase unwrapping, since the data correlation for the 1D phase unwrapping is very limited. In a recent work, we proposed the 1D phase unwrapping algorithm by combining quasi-Gramian matrix and deep convolutional neural network (DCNN). The obtained result is very robust and can work very stably under low signal-to-noise ratio (SNR) level. But the required parameter amount is large being about 10(7) making itself inadequate for resource limited computation platforms. In this work, a lightweight phase unwrapping algorithm based on long short-term memory (LSTM) network is proposed, which utilizes only 10(6) parameter amounts, being 1 order smaller than the DCNN. Simulation results demonstrate that in the SNR range of 0 to 16 dB, the LSTM-based method shows comparable or better performance compared with the DCNN method, and for the very low SNR of -2 dB, only small performance decrease is observed. The generalization ability of the proposed method is also verified by using the experimental data collected from an actual phase-sensitive optical time domain reflectometry (phi-OTDR) system.
Keywords:
Signal to noise ratio
Training
Signal processing algorithms
Machine learning algorithms
Optical fiber sensors
Mathematical models
Prediction algorithms
Deep learning
interferometric fiber sensors
LSTM network
phase unwrapping
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