Convolutional Neural Networks based Denoising for Indoor Localization

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. I...

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Bibliographic Details
Published inIEEE Vehicular Technology Conference pp. 1 - 6
Main Authors Njima, Wafa, Chafii, Marwa, Nimr, Ahmad, Fettweis, Gerhard
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2021
Subjects
Convolutional Neural Networks (CNN)
Euclidean distance
Indoor localization
Location awareness
Matrix completion
Measurement uncertainty
Minimization
Noise reduction
Received Signal Strength Indicator (RSSI)
Simulation
Trilateration
Vehicular and wireless technologies
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Summary: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.
ISSN:2577-2465
DOI:10.1109/VTC2021-Spring51267.2021.9448839