Journals
  Publication Years
  Keywords
Search within results Open Search
Please wait a minute...
For Selected: Toggle Thumbnails
PON Monitoring Scheme Based on TGD-OFDR with High Spatial Resolution and Dynamic Range
ZHU Yidai, FAN Xinyu, ZHU Songlin, DONG Jiaxing, LI Guoqiang, HE Zuyuan
ZTE Communications    2025, 23 (4): 3-9.   DOI: 10.12142/ZTECOM.202504002
Abstract38)   HTML1)    PDF (1293KB)(3)       Save

Conventional optical time-domain reflectometry (OTDR) schemes for passive optical network (PON) link monitoring are limited by insufficient dynamic range and spatial resolution. The expansion of PONs, with increasing optical network units (ONUs) and cascaded splitters, imposes even more stringent demands on the dynamic range of monitoring systems. To address these challenges, we propose a time-gated digital optical frequency-domain reflectometry (TGD-OFDR) system for PON monitoring that effectively decouples the inherent coupling between spatial resolution and pulse width. The proposed system achieves both high spatial resolution (~0.3 m) and high dynamic range (~30 dB) simultaneously, marking a significant advancement in optical link monitoring.

Table and Figures | Reference | Related Articles | Metrics
Practical Pattern Recognition System for Distributed Optical Fiber Intrusion Monitoring Based on Ф-COTDR
CAO Cong, FAN Xinyu, LIU Qingwen, HE Zuyuan
ZTE Communications    2017, 15 (3): 52-55.   DOI: 10.3969/j.issn.1673-5188.2017.03.007
Abstract237)   HTML2)    PDF (416KB)(210)       Save

At present, the demand for perimeter security system is increasing greatly, especially for such system based on distributed optical fiber sensing. This paper proposes a perimeter security monitoring system based on phase-sensitive coherent optical time domain reflectometry(Ф-COTDR) with the practical pattern recognition function. We use fast Fourier transform (FFT) to exact features from intrusion events and a multi-class classification algorithm derived from support vector machine (SVM) to work as a pattern recognition technique. Five different types of events are classified by using a classification algorithm based on SVM through a three-dimensional feature vector. Moreover, the identification results of the pattern recognition system show that an identification accurate rate of 92.62% on average can be achieved.

Table and Figures | Reference | Related Articles | Metrics