| Title | Detection of Events in OTDR Data via Variational Mode Decomposition and Hilbert Transform |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Liu, Bo, Kong, Qingshan, Huang, Weiqing, Guo, Shaoying |
| Conference Name | 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) |
| Keywords | composability, compositionality, decomposition, event detection, fiber events, Fourier transforms, Hilbert transform (HT), Integrated optics, location awareness, Metrics, optical fiber communication, Optical fibers, Optical time domain reflectometry (OTDR), pubcrawl, reflectometry, Variational Mode Decomposition (VMD) |
| Abstract | Optical time domain reflectometry (OTDR) plays an important role in optical fiber communications. To improve the performance of OTDR, we propose a method based on the Variational Mode Decomposition (VMD) and Hilbert transform (HT) for fiber events detection. Firstly, the variational mode decomposition is applied to decompose OTDR data into some intrinsic mode functions (imfs). To determine the decomposition mode number in VMD, an adaptive estimation method is introduced. Secondly, the Hilbert transform is utilized to obtain the instantaneous amplitude of the imf for events localization. Finally, the Dynamic Time Warping (DTW) is used for identifying the type of event. Experimental results show that the proposed method can locate events accurately. Compared with the Short-Time Fourier Transform (STFT) method, the VMD-HT method presents a higher accuracy in events localization, which indicates that the method is effective and applicable. |
| DOI | 10.1109/ICCCS52626.2021.9449184 |
| Citation Key | liu_detection_2021 |
Detection of Events in OTDR Data via Variational Mode Decomposition and Hilbert Transform