Train Detection and Tracking in Optical Time Domain Reflectometry (OTDR) Signals

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• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9796
• 期：1
• 页码：320-331
• 全文大小：1,538 KB
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• 作者单位：Adam Papp (15)
  Christoph Wiesmeyr (15)
  Martin Litzenberger (15)
  Heinrich Garn (15)
  Walter Kropatsch (16)
  
  15. Digital Safety and Security Department, Austrian Institute of Technology GmbH, Vienna, Austria
  16. Pattern Recognition and Image Processing Group, Vienna University of Technology, Vienna, Austria
  
• 丛书名：Pattern Recognition
• ISBN：978-3-319-45886-1
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9796

文摘

We propose a novel method for the detection of vibrations caused by trains in an optical fiber buried nearby the railway track. Using optical time-domain reflectometry vibrations in the ground caused by different sources can be detected with high accuracy in time and space. While several algorithms have been proposed in the literature for train tracking using OTDR signals they have not been tested on longer recordings. The presented method learns the characteristic pattern in the Fourier domain using a support vector machine (SVM) and it becomes more robust to any kind of noise and artifacts in the signal. The point-based causal train tracking has two stages to minimize the influence of false classifications of the vibration detection. Our technical contribution is the evaluation of the presented algorithm based on two hour long recording and demonstration of open problems for commercial usage.