We propose to employ multiple tiny neural network (NN) models to localize a mobile client in large or complex indoor environments. These models are trained in a self-supervised fashion by exploiting the location labels obtained from a geometry-based bootstrapping localization algorithm, thus relieving the burden of training data collection. We further propose a scheme to switch to the right NN model in order to keep localizing a mobile client accurately as it moves through the indoor space.
Multiple Self-Supervised Tiny Neural Networks for mmWave Localization in Complex Indoor Areas / Shastri, Anish; Casari, Paolo. - 2023-September:(2023), pp. 398-399. (Intervento presentato al convegno 20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023 tenutosi a Madrid, Spain nel 11 September - 14 September 2023) [10.1109/secon58729.2023.10287420].
Multiple Self-Supervised Tiny Neural Networks for mmWave Localization in Complex Indoor Areas
Shastri, Anish
;Casari, Paolo
2023-01-01
Abstract
We propose to employ multiple tiny neural network (NN) models to localize a mobile client in large or complex indoor environments. These models are trained in a self-supervised fashion by exploiting the location labels obtained from a geometry-based bootstrapping localization algorithm, thus relieving the burden of training data collection. We further propose a scheme to switch to the right NN model in order to keep localizing a mobile client accurately as it moves through the indoor space.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
