Angela Rotunno
Machine Learning Techniques for Collaborative, Multi-agent GNSS Positioning in IoT devices.
Rel. Alex Minetto, Daniele Jahier Pagliari. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2023
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Abstract
Nowadays, Global Navigation Satellite Systems (GNSS) receivers are embedded in a variety of electronic devices, and a growing number of users depend on them to navigate themselves to a destination. GNSS technology allows the user to estimate its position through multilateration, which is based on satellite detection, the estimation of the signal Time-of-Arrival (ToA), and the subsequent measurement of the receiver-to-satellite ranges (commonly referred to as “pseudoranges”). The low power of GNSS signals makes the continuous satellite’s signal tracking a challenging task for the receiver, especially considering the tight constraints on resource usage. Internet-of-Things (IoT) electronics host “by definition” low-power-consumption network connectivity, and they could enable new patterns for the Position Velocity and Time (PVT) estimation, based on collaborative, multi-agent Positioning, Navigation, and Timing (PNT) methods that would not imply a continuous operation of the embedded GNSS receiver.
This study aims to understand whether Machine Learning techniques could support such paradigms for the PVT estimation in IoT devices, in the attempt to avoid the need for costly continuous signal tracking, demodulation of the navigation message, and pseudorange construction and correction, by exploiting the information made available by a set of networked collaborative users
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