发帖
雷达卡
摘要翻译：
为了规划快速响应和最小化运营成本，无源光网络运营商要求自动检测和识别光分配网络中可能发生的故障。在这项工作中，我们提出了一种新的基于传统光时域反射法和参考道的远程故障分析方法--DSP增强OTDR。根据Neyman-Pearson准则，结合数学形式，我们导出了一致最强的检测测试，这些检测测试是最优的。为了识别故障类型和充分表征故障，在检测阶段之后是对其特征参数的估计，如回波损耗和插入损耗。实验证明，该方法能够在事件死区内检测故障，克服了传统事件标记算法的不足。
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英文标题：
《DSP-Enhanced OTDR for Detection and Estimation of Events in PONs》
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作者：
Manuel P. Fernandez, Laureano A. Bulus Rossini, Juan Pablo Pascual,
Pablo A. Costanzo Caso
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
To plan a rapid response and minimize operational costs, passive optical network operators require to automatically detect and identify faults that may occur in the optical distribution network. In this work, we present DSP-Enhanced OTDR, a novel methodology for remote fault analysis based on conventional optical time-domain reflectometry complemented with reference traces. Together with the mathematical formalism, we derive the detection tests that result to be uniformly most powerful, which are optimal according to the Neyman-Pearson criterion. To identify the type of fault and fully characterize it, the detection stage is followed by the estimation of its characteristic parameters, such as return loss and insertion loss. We experimentally demonstrate that this approach allows to detect faults inside the event dead zone, which overcomes the shortage of conventional event-marking algorithms.
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PDF链接：
https://arxiv.org/pdf/1801.06485
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