Machine Learning-assisted GNSS Interference Monitoring through Crowdsourcing

Raichur NL, Brieger T, Jdidi D, Feigl T, Van Der Merwe JR, Ghimire B, Ott F, Ruegamer A, Felber W (2022)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

Pages Range: 1151-1175

Conference Proceedings Title: Proc. Intl. Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+)

Event location: Denver, CO US

DOI: 10.33012/2022.18492

Abstract

Interference signals harm Global Navigation Satellite System (GNSS) processing. Therefore, interference signals need to be mitigated or the interference sources need to be localized and removed. A monitoring network is needed to localize an interference source, and the more monitoring receivers are available the better. Luckily, smartphones are ubiquitous and typically include a GNSS receiver for positioning, making them ideal as a monitoring network through crowdsourcing. Further, with recent advances in the Android ecosystem, GNSS measurement support is mandatory to capture the raw data for many real-time tracking applications. This opens a door to more advanced GNSS processing and applications. Since Rel. 9, the Third-Generation Partnership Project (3GPP) supports the distribution of assistance information for mobile devices, e.g., GNSS signal to improve the quality of network services or even precise positioning. It is possible, that such crowdsourcing approaches could provide immense benefits to GNSS integrity if it is included in future 3GPP releases. Consider a typical scenario: a moving interference affects the signal reception of two UEs within a certain area. The signaling changes dynamically so that the situation requires monitoring by more than one entity, motivating the real-time and widespread monitoring to adapt to the dynamic environment. Therefore, we propose an adaptive framework to detect, (potentially) classify, and localize such sources of interference using artificial intelligence and crowdsourcing techniques along our processing chain.
The idea of our paper is that mobile devices such as smartphones may detect such local GNSS interference events using data-driven methods and their reliability and make them available to the global network, e.g., by using 5G. Additionally, our crowdsourcing framework allows for the collection of interference events from many devices to characterize their nature and impact. Our framework publishes the collected information to all other mobile devices in the network to significantly improve the accuracy of localization and signaling and reduce unnecessary processing of corrupted signals.

Authors with CRIS profile

Tobias Brieger Lehrstuhl für Informatik 1 (IT-Sicherheitsinfrastrukturen) Tobias Feigl Lehrstuhl für Informatik 2 (Programmiersysteme)

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Raichur, N.L., Brieger, T., Jdidi, D., Feigl, T., Van Der Merwe, J.R., Ghimire, B.,... Felber, W. (2022). Machine Learning-assisted GNSS Interference Monitoring through Crowdsourcing. In Proc. Intl. Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+) (pp. 1151-1175). Denver, CO, US.

MLA:

Raichur, Nisha Lakshmana, et al. "Machine Learning-assisted GNSS Interference Monitoring through Crowdsourcing." Proceedings of the Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+), Denver, CO 2022. 1151-1175.

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