Real-Time Train Tracking from Distributed Acoustic Sensing Data
In the context of railway safety, it is crucial to know the positions of all trains moving along the infrastructure. In this contribution, we present an algorithm that extracts the positions of moving trains for a given point in time from Distributed Acoustic Sensing (DAS) signals. These signals are...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/2/448 |
| _version_ | 1818806529354104832 |
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| author | Christoph Wiesmeyr Martin Litzenberger Markus Waser Adam Papp Heinrich Garn Günther Neunteufel Herbert Döller |
| author_facet | Christoph Wiesmeyr Martin Litzenberger Markus Waser Adam Papp Heinrich Garn Günther Neunteufel Herbert Döller |
| author_sort | Christoph Wiesmeyr |
| collection | DOAJ |
| description | In the context of railway safety, it is crucial to know the positions of all trains moving along the infrastructure. In this contribution, we present an algorithm that extracts the positions of moving trains for a given point in time from Distributed Acoustic Sensing (DAS) signals. These signals are obtained by injecting light pulses into an optical fiber close to the railway tracks and measuring the Rayleigh backscatter. We show that the vibrations of moving objects can be identified and tracked in real-time yielding train positions every second. To speed up the algorithm, we describe how the calculations can partly be based on graphical processing units. The tracking quality is assessed by counting the inaccurate and lost train tracks for two different types of cable installations. |
| first_indexed | 2024-12-18T19:11:13Z |
| format | Article |
| id | doaj.art-a70d47d0b3844e6a9e887a8f33362e7b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-18T19:11:13Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a70d47d0b3844e6a9e887a8f33362e7b2022-12-21T20:56:15ZengMDPI AGApplied Sciences2076-34172020-01-0110244810.3390/app10020448app10020448Real-Time Train Tracking from Distributed Acoustic Sensing DataChristoph Wiesmeyr0Martin Litzenberger1Markus Waser2Adam Papp3Heinrich Garn4Günther Neunteufel5Herbert Döller6AIT Austrian Institute of Technology GmbH, Giefinggasse 4, 1210 Vienna, AustriaAIT Austrian Institute of Technology GmbH, Giefinggasse 4, 1210 Vienna, AustriaAIT Austrian Institute of Technology GmbH, Giefinggasse 4, 1210 Vienna, AustriaAIT Austrian Institute of Technology GmbH, Giefinggasse 4, 1210 Vienna, AustriaAIT Austrian Institute of Technology GmbH, Giefinggasse 4, 1210 Vienna, AustriaNBG Fosa GmbH, Johannesgasse 15, 1010 Vienna, AustriaNBG Fosa GmbH, Johannesgasse 15, 1010 Vienna, AustriaIn the context of railway safety, it is crucial to know the positions of all trains moving along the infrastructure. In this contribution, we present an algorithm that extracts the positions of moving trains for a given point in time from Distributed Acoustic Sensing (DAS) signals. These signals are obtained by injecting light pulses into an optical fiber close to the railway tracks and measuring the Rayleigh backscatter. We show that the vibrations of moving objects can be identified and tracked in real-time yielding train positions every second. To speed up the algorithm, we describe how the calculations can partly be based on graphical processing units. The tracking quality is assessed by counting the inaccurate and lost train tracks for two different types of cable installations.https://www.mdpi.com/2076-3417/10/2/448dasfiber optic sensingtrain trackingpattern recognition |
| spellingShingle | Christoph Wiesmeyr Martin Litzenberger Markus Waser Adam Papp Heinrich Garn Günther Neunteufel Herbert Döller Real-Time Train Tracking from Distributed Acoustic Sensing Data Applied Sciences das fiber optic sensing train tracking pattern recognition |
| title | Real-Time Train Tracking from Distributed Acoustic Sensing Data |
| title_full | Real-Time Train Tracking from Distributed Acoustic Sensing Data |
| title_fullStr | Real-Time Train Tracking from Distributed Acoustic Sensing Data |
| title_full_unstemmed | Real-Time Train Tracking from Distributed Acoustic Sensing Data |
| title_short | Real-Time Train Tracking from Distributed Acoustic Sensing Data |
| title_sort | real time train tracking from distributed acoustic sensing data |
| topic | das fiber optic sensing train tracking pattern recognition |
| url | https://www.mdpi.com/2076-3417/10/2/448 |
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