Mattia Morin
Deep learning and data augmentation techniques for indoor environment characterization via UWB technology.
Rel. Marcello Chiaberge, Marina Mondin. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (19MB) | Preview |
Abstract
Ultra-wideband is a radio-based communication technology for short-range use and fast and stable transmission of data. Its main characteristics are extremely large bandwidth, very low signal power density, robustness against fading, and low cost. These features make ultra-wideband suitable for indoor localization applications. Referring to the positioning accuracy, it is around five and twenty-five times better than adopting Bluetooth Low Energy (BLE) beacons or Wi-Fi, respectively. Despite these promising results, ultra-wideband localization accuracy robustly degrades when moving to non-ideal conditions, including Non-Line-Of-Sight and the presence of dynamic environmental factors. This work aims at characterizing an indoor environment via channel impulse responses retrieved by ultra-wideband technology, opening the doors to future research in terms of sensor fusion techniques for improving indoor localization accuracy and indoor channel characterization.
As a first step of this work, a sufficiently large dataset is collected in a typical indoor scenario, including static, dynamic, Line-Of-Sight and Non-Line-Of-Sight conditions
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Corso di laurea
Classe di laurea
Ente in cotutela
Aziende collaboratrici
URI
![]() |
Modifica (riservato agli operatori) |
