Iman Ebrahimi Mehr
Detecting Interference in GNSS using Machine Learning.
Rel. Fabio Dovis. Politecnico di Torino, Corso di laurea magistrale in Ict For Smart Societies (Ict Per La Società Del Futuro), 2021
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Abstract
Global Navigation Satellite Systems have truly established themselves as part of the critical infrastructure in today’s world. GNSS positioning, navigation, and timing services have a significant role in various applications. These GNSS signals are transmitted from satellites that take place over 22000 km far from the earth. The power of the received signal in the antenna is weak and vulnerable to interference, causing reduced positioning accuracy or even the complete lack of position availability. Thus, it is critical and essential for several applications to detect interference and recognize their type to mitigate and guarantee a high accuracy positioning. Various types of intentional interferences disturb the GNSS signal on the way of receivers.
In this paper, we focused on the CHIRP signal, one of the frequent jamming signals
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