Chen Xiaojuan, Yu Haoyu
School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel). 2023 Sep 29;23(19):8166. doi: 10.3390/s23198166.
Brillouin optical time domain reflectometry (BOTDR) detects fiber temperature and strain data and represents one of the most critical ways of identifying abnormal conditions such as ice coverage and lightning strikes on optical fiber composite overhead ground wire (OPGW) cable. Existing BOTDR extracts brillouin frequency shift (BFS) features with cumulative averaging and curve fitting. BFS feature extraction is slow for long-distance measurements, making realizing real-time measurements on fiber optic cables challenging. We propose a fast feature extraction method for block matching and 3D filtering (BM3D) + Sobel brillouin scattering spectroscopy (BGS). BM3D takes the advantage of non-local means (NLM) and wavelet denoising (WD) and utilizes the spatial-domain non-local principle to enhance the denoising in the transform domain. The global filtering capability of BM3D is utilized to filter out the low cumulative average BGS noise and the BFS feature extraction is completed using Sobel edge detection. Simulation verifies the feasibility of the algorithm, and the proposed method is embedded in BOTDR to measure 30 km of actual OPGW line. The experimental results show that under the same conditions, the processing time of this method is reduced by 37 times compared to that with the 50,000-time cumulative averaging + levenberg marquardt (LM) algorithm without severe distortion of the reference resolution. The method improves the sensor demodulation speed by using image processing technology without changing the existing hardware equipment, which is expected to be widely used in the new generation of BOTDR.
布里渊光时域反射仪（BOTDR）可检测光纤温度和应变数据，是识别诸如光纤复合架空地线（OPGW）电缆上的覆冰和雷击等异常情况的最关键方法之一。现有的BOTDR通过累积平均和曲线拟合来提取布里渊频移（BFS）特征。对于长距离测量，BFS特征提取速度较慢，这使得实现对光缆的实时测量具有挑战性。我们提出了一种用于块匹配和3D滤波（BM3D）+索贝尔布里渊散射光谱（BGS）的快速特征提取方法。BM3D利用了非局部均值（NLM）和小波去噪（WD）的优势，并利用空间域非局部原理来增强变换域中的去噪效果。利用BM3D的全局滤波能力滤除低累积平均BGS噪声，并使用索贝尔边缘检测完成BFS特征提取。仿真验证了该算法的可行性，并将所提出的方法嵌入到BOTDR中以测量30公里的实际OPGW线路。实验结果表明，在相同条件下，与采用50000次累积平均+列文伯格-马夸尔特（LM）算法相比，该方法的处理时间减少了37倍，且参考分辨率没有严重失真。该方法通过使用图像处理技术提高了传感器解调速度，而无需改变现有硬件设备，有望在新一代BOTDR中得到广泛应用。
