Deep Deconvolution Applied to Distributed Acoustic Sensing for Traffic Analysis - Archive ouverte HAL
Communication Dans Un Congrès Année : 2022

Deep Deconvolution Applied to Distributed Acoustic Sensing for Traffic Analysis

Martijn van den Ende
  • Fonction : Auteur
Anthony Sladen
  • Fonction : Auteur
Cédric Richard

Résumé

Distributed Acoustic Sensing (DAS) is a nascent technology that facilitates the measurement of vibrations along fibre-optic telecommunication cables, which has numerous novel applications in many domains of science and engineering. In the present study, we use DAS to analyse traffic along a fibre-optic cable deployed along a major road in Nice, France. For the objective of estimating the speed of individual vehicles, we propose a MUSIC beamforming algorithm, which exhibits superior performance when applied to data that has been deconvolved with a Deep Learning model. The accuracy of the speed estimation is in the range of 0.14-0.25 km/h , which is at least one order of magnitude better than conventional methods. DAS therefore has great potential in urban traffic analysis applications.
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Dates et versions

hal-04242543 , version 1 (15-10-2023)

Identifiants

Citer

Martijn van den Ende, Andre Ferrari, Anthony Sladen, Cédric Richard. Deep Deconvolution Applied to Distributed Acoustic Sensing for Traffic Analysis. 2022 30th European Signal Processing Conference (EUSIPCO), Aug 2022, Belgrade, Serbia. pp.882-886, ⟨10.23919/EUSIPCO55093.2022.9909792⟩. ⟨hal-04242543⟩
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