Abstract
In the past years, many techniques have been researched and developed to detect and identify the interference sources of Global Navigation Satellite System (GNSS) signals. In this paper, we utilize a simple and portable application to map interference sources in real-time. The results are promising and show the potential of the crowdsourcing for monitoring and mapping GNSS interference distribution.
Highlights
Radio-frequency interference (RFI), either unconscious or intentional, is one of the most feared events that can disrupt the functionalities of a Global Navigation Satellite System (GNSS) receiver and the user-level applications dependent on it [1,2]
Bands, through various data collections performed ad-hoc for testing specific detection, mitigation, or localization algorithms [9,10]. Those data collection campaigns are unfit for the applications mentioned before, because they are meant to offer a representative sample of the average interference scenario in a certain environment in non-real-time, while they are unable to offer a real-time picture of the RFI nearby a certain position
We developed an Android app to run a GNSS software receiver, able to detect in real-time
Summary
Radio-frequency interference (RFI), either unconscious or intentional, is one of the most feared events that can disrupt the functionalities of a Global Navigation Satellite System (GNSS) receiver and the user-level applications dependent on it [1,2]. Android smartphones have started to provide ‘raw’ GNSS measurements, namely carrier and code measurements, decoded navigation message, as well as Automatic Gain Control (AGC) levels, through an ad-hoc Application Programming Interface (API) [14,15] This innovation has followed the idea of opening the GNSS signal processing chain before the final on-chip Position-Velocity and Time (PVT) solution, to allow third-party processing capabilities based on non-standard algorithms to improve GNSS performance: for example, aided positioning, differential positioning, precise point positioning. The availability of such measurements, together with the intrinsic network connectivity, can be exploited to implement forms of distributed interference monitoring, as investigated in [16,17].
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