Deep Deconvolution Applied to Distributed Acoustic Sensing for Traffic Analysis
- Others:
- Géoazur (GEOAZUR 7329) ; Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur ; Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])
- Joseph Louis LAGRANGE (LAGRANGE) ; Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur ; Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)
- ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)
- ANR-19-CE48-0002,DARLING,Adaptation et apprentissage distribués pour les signaux sur graphe(2019)
Description
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.
Additional details
- URL
- https://hal.science/hal-04242543
- URN
- urn:oai:HAL:hal-04242543v1
- Origin repository
- UNICA