Li Baocheng, Tan Zhi-Wei, Shum Perry Ping, Wang Chenlu, Zheng Yu, Wong Liang Jie
Opt Express. 2021 Mar 1;29(5):7110-7123. doi: 10.1364/OE.413443.
In quasi-distributed fiber Bragg grating (FBG) sensor networks, challenges are known to arise when signals are highly overlapped and thus hard to separate, giving rise to substantial error in signal demodulation. We propose a multi-peak detection deep learning model based on a dilated convolutional neural network (CNN) that overcomes this problem, achieving extremely low error in signal demodulation even for highly overlapped signals. We show that our FBG demodulation scheme enhances the network multiplexing capability, detection accuracy and detection time of the FBG sensor network, achieving a root-mean-square (RMS) error in peak wavelength determination of < 0.05 pm, with a demodulation time of 15 ms for two signals. Our demodulation scheme is also robust against noise, achieving an RMS error of < 0.47 pm even with a signal-to-noise ratio as low as 15 dB. A comparison on our high-performance computer with existing signal demodulation methods shows the superiority in RMS error of our dilated CNN implementation. Our findings pave the way to faster and more accurate signal demodulation methods, and testify to the substantial promise of neural network algorithms in signal demodulation problems.
在准分布式光纤布拉格光栅（FBG）传感器网络中，当信号高度重叠从而难以分离时，就会出现挑战，这会在信号解调中产生大量误差。我们提出了一种基于扩张卷积神经网络（CNN）的多峰检测深度学习模型，该模型克服了这个问题，即使对于高度重叠的信号，在信号解调中也能实现极低的误差。我们表明，我们的FBG解调方案提高了FBG传感器网络的网络复用能力、检测精度和检测时间，在峰值波长确定中实现了均方根（RMS）误差<0.05 pm，两个信号的解调时间为15 ms。我们的解调方案对噪声也具有鲁棒性，即使在信噪比低至15 dB的情况下，RMS误差也<0.47 pm。在我们的高性能计算机上与现有信号解调方法进行的比较表明，我们的扩张CNN实现方法在RMS误差方面具有优越性。我们的研究结果为更快、更准确的信号解调方法铺平了道路，并证明了神经网络算法在信号解调问题中的巨大潜力。
