Real-Time Train Tracking from Distributed Acoustic Sensing Data

Abstract

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.

Evaluating seismic beamforming capabilities of distributed acoustic sensing arrays

“…Owing to the nature of the measurement principle of DAS (i.e. measuring strains rather than particle motions), the directional sensitivity of the fibre to P and S waves is different from nodal seismometers (Kuvshinov, 2016;Zhan, 2020). For a gauge length that is much smaller than the seismic wavelength, the DAS strain rate is proportional to cos 2 θ for a P wave or SV wave, and sin 2θ for an SH wave, assuming a plane wave with incidence angle θ relative to the fi-bre (Martin et al, 2018).…”

“…The recent emergence of fibre-optic distributed acoustic sensing (DAS; Hartog, 2017;Zhan, 2020) has opened up a plethora of possibilities and applications in seismic and transient deformation monitoring. Fibre-optic cables are relatively inexpensive, require little to no maintenance, and can be deployed in environments that were previously impractical for or inaccessible to traditional seismometers, such as urban environments (Dou et al, 2017;Fang et al, 2020), glaciers and permafrost regions (Ajo-Franklin et al, 2017;Walter et al, 2020), deep boreholes (Cole et al, 2018;Lellouch et al, 2019), and in lakes and submarine environments (Lindsey et al, 2019;Sladen et al, 2019) -see also Zhan (2020) for a concise review of applications in geosciences. DAS thus has an enormous potential to complement or replace seismometer arrays (Jousset et al, 2018).…”

“…DAS thus has an enormous potential to complement or replace seismometer arrays (Jousset et al, 2018). However, the measurement principles of DAS are inherently different from those of conventional seismometers (Zhan, 2020), which presents new challenges in interpreting DAS data. Traditional array processing techniques, such as seismic beamforming, need to be re-evaluated for the application to DAS data.…”

Editorial on Special Issue “Modern Applications in Optics and Photonics: From Sensing and Analytics to Communication”

“…In terms of distributed sensing, one paper is presented by Wiesmeyr et al [8]. In their research article, the real-time train tracking from distributed acoustic sensing data is reported by presenting an algorithm that extracts the positions of moving trains for a given point in time from Distributed Acoustic Sensing (DAS) signals.…”

Performance, Flexibility and Usability Enhancement of the Fiber Optic Vibration Sensor

“…into extrinsic and intrinsic sensor classes, point or distributed sensors, etc. The fiber-based sensors are used in many areas of industry, mainly for health structure monitoring, ( [3], [4], [5], [6]) but its potential is also in a security area to detect, locate and optionally classify intruders.…”