Convolutional Neural Networks based Denoising for Indoor Localization

Wafa Njima (1) , Marwa Chafii (1) , Ahmad Nimr (2) , Gerhard Fettweis (2)

1 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
2 Technische Universität Dresden, Vodafone Chair Mobile Communications Systems

Indoor localization can be based on a matrix of pairwise distances between nodes to localize and reference nodes. This matrix is usually not complete, and its completion is subject to distance estimation errors as well as to the noise resulting from received signal strength indicator measurements. In this paper, we propose to use convolutional neural networks in order to denoise the completed matrix. A trilateration process is then applied on the recovered euclidean distance matrix (EDM) to locate an unknown node. This proposed approach is tested on a simulated environment, using a real propagation model based on measurements, and compared with the classical matrix completion approach, based on the adaptive moment estimation method, combined with trilateration. The simulation results show that our system outperforms the classical schemes in terms of EDM recovery and localization accuracy.

Mots clés: Convolutional Neural Networks (CNN); Indoor localization; Matrix completion; Received Signal Strength Indicator (RSSI); Trilateration

Domaines: Informatique [cs]; Mathématiques [math]

Wafa Njima, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis. Convolutional Neural Networks based Denoising for Indoor Localization. The 2021 IEEE 93rd Vehicular Technology Conference: VTC2021-Spring, Apr 2021, Helsinki (on line), Finland. ⟨hal-03170579⟩