A novel image representation of GNSS correlation for deep learning multipath detection - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Array Année : 2022

A novel image representation of GNSS correlation for deep learning multipath detection

(1) , (2, 1) , (1)
1
2

Résumé

This paper proposes a novel framework for multipath prediction in Global Navigation Satellite System (GNSS) signals. The method extends from dataset generation to deep learning inference through Convolutional Neural Network (CNN). The process starts at the output of the correlation stage of the GNSS receiver. Correlations of the received signal with a local replica over a (Doppler shift, propagation delay)-grid are mapped into grey scale 2D images. They depict the received information possibly contaminated by multipath propagation. The images feed a CNN for automatic feature construction and multipath pattern detection. The issue of unavailability of a large amount of supervised data required for CNN training has been overcome by the development of a synthetic data generator. It implements a well-established and documented theoretical model. A comparison of synthetic data with real samples is proposed. The complete framework is tested for various signal characteristics and algorithm parameters. The prediction accuracy does not fall below 93% for C/N0 ratio as low as 36 dBHz, corresponding to poor receiving conditions. In addition, the model turns out to be robust to the reduction of image resolution. Its performance is also measured and compared with an alternative Support Vector Machines (SVM) technique. The results show the undeniable superiority of the proposed CNN algorithm over the SVM benchmark.
Fichier principal
Vignette du fichier
CNN_GNSS_multipath.pdf (2.18 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03641028 , version 1 (14-04-2022)

Identifiants

Citer

Antoine Blais, Nicolas Couellan, Evgenii Munin. A novel image representation of GNSS correlation for deep learning multipath detection. Array, inPress, ⟨10.1016/j.array.2022.100167⟩. ⟨hal-03641028⟩
255 Consultations
66 Téléchargements

Altmetric

Partager

Gmail Facebook Twitter LinkedIn More