基于压缩感知的BOTDA性能增强方法

为了提高布里渊光时域分析系统(BOTDA)在长距离监测应用中的实时性,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,所提算法将信噪比提升了6.37 dB,优于累加平均次数3000时的10.13 dB,对应测量时间减少了1/30;采用8 MHz步长数据重构布里渊增益谱,该方法的重构结果与4 MHz步长数据的相关系数为0.9992,对应扫频时间减少了一半。所提算法在保证测量精度的同时提升了测量实时性。

Performance Enhancement Method of BOTDA Based on Compressed Sensing

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