Projecte ofert
Títol: Efficient Fingerprinting Radio Map Construction in Harsh Indoor Environments
Per assignar-vos el projecte us heu de dirigir al director/a perquè us l'assigni.
Director/a: ZOLA, ENRICA VALERIA
Departament: ENTEL
Títol: Efficient Fingerprinting Radio Map Construction in Harsh Indoor Environments
Data inici oferta: 26-06-2026 Data finalització oferta: 26-02-2027
Estudis d'assignació del projecte:
DG ENG AERO/TELEMÀT- DG ENG SISTE/TELEMÀT
| Tipus: Individual | |
| Lloc de realització: EETAC | |
| Segon director/a (UPC): MARTIN ESCALONA, ISRAEL | |
| Paraules clau: | |
| IEEE 802.11, indoor positioning, Wi-Fi, RTT, efficient radio map construction, NLoS scenarios | |
| Descripció del contingut i pla d'activitats: | |
| The demand for indoor location services has exploded due to the wide range of applications they have to offer. For this purpose, classic positioning technologies (GPS among others) are not able to provide an effective response due to the low level of coverage they achieve. There are commercial solutions based on the use of Wi-Fi technology to offer this service, as it is widely deployed and accessible from practically any mobile device on the market, thus reducing the costs associated with its implementation.
The fingerprinting positioning technique has proved to provide the best results indoors when applied to Wi-Fi RTT. However, its main limitation lies in the need to perform a manual mapping of the site prior to deployment. In order to have up-to-date information on the environment, mapping must be repeated frequently in these spaces due to their changing nature. The time and resource cost of such a procedure hampers the large-scale deployment of this technique. Recently, we proved that a Gaussian prediction model is able to recreate fingerprints in indoor scenarios with line of sight (LoS). Taking this previous work, with this project we want to go a step further, improving the model for harsh (e.g., Non-LoS) environments. The main objective of the proposed Master Thesis is to implement a hybrid indoor location solution based on Wi-Fi RTT fingerprinting, in which the impact of the mapping survey is minimised by scanning only certain points of the harsh scenario and making a prediction of the remaining map. |
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| Orientació a l'estudiant: | |
| Knowledge on Wi-Fi 802.11 protocols. Good knowledge of Python. Basic knowledge on Gaussian Process Regression (GPR) models. | |
| Requereix activitats hardware: No | |
| Requereix activitats software: Sí Sistema operatiu: Disc (Gb): | |
| Horari d'atenció a estudiants per a l'assignació de projecte: Send me an email (enrica.zola@upc.edu) |
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