基于压缩感知的BOTDA性能增强方法

Performance Enhancement Method of BOTDA Based on Compressed Sensing

摘要:

为了提高布里渊光时域系统在长距离监测应用中的实时性，本文提出了一种基于压缩感知的布里渊光时域系统实时性增强方法。该方法包含稀疏表示、随机采样和信号重构三个部分。首先采用K-均值奇异值分解算法获得布里渊增益谱的稀疏表示，然后通过高斯随机采样和正交匹配追踪算法进行布里渊增益谱重构。为了验证所提方法的性能，仿真生成了不同信噪比水平的布里渊增益谱，搭建了45 km的布里渊光时域系统进行温度传感实验。仿真和实验结果表明：在累加平均次数为100时，本文所提算法将信噪比提升了6.37dB，优于累加平均次数3000时的 10.13dB，对应测量时间减少了1/30；采用8MHz步长数据重构布里渊增益谱，本文方法的重构结果与4MHz步长数据的相关系数为0.9992，对应扫频时间减少了一半。本文所提算法在保证测量精度的同时提升了测量实时性。

Abstract:

In order to improve the real-time performance of Brillouin optical-time domain analyzer (BOTDA), a method based on compressed sensing is proposed in this paper. It contains sparse representation, random sampling and signal reconstruction. Firstly, the method obtained the sparse representation of Brillouin gain spectrum (BGS) by k-means singular value decomposition algorithm, and then BGS can be reconstructed successfully with Gaussian random matrix and orthogonal matching pursuit algorithms. To verify the performance of the proposed method, BGS at different SNR were generated and a 45 km BOTDA was built for temperature experiments. Simulation and experiments show that the reconstructed SNR increase 6.37dB when the average times is 100, which is better than 10.13dB at 3000 average times. We can conclude that the measurement time was reduced 1/30. Besides, the correlation coefficient between reconstructed BGS with scanning step of 8MHz and experimental BGS with 4MHz is 0.9992, which makes the sweeping time decrease 1/2. The compressed sensing method not only ensures the measurement accuracy but also improves the real-time performance of BOTDA.