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Session B1a: GNSS Integrity and Augmentation

Catalogue of GNSS Signal Anomalies Using High-Rate Differential Code bias Estimates
Gerardo Allende-Alba, Institute of Communications and Navigation, German Aerospace Center (DLR); Steffen Thoelert, DLR and RWTH Aachen University; Peter Steigenberger, DLR; Michael Meurer, DLR & RWTH Aachen University
Location: Beacon A
Date/Time: Tuesday, Jan. 23, 2:12 p.m.

Peer Reviewed

For applications such as autonomous car driving and unmanned aerial vehicles (UAV), ensuring reliability, high accuracy and integrity of navigation services is crucial. This requires a sufficiently accurate modeling of potential threats and error contributions as well as monitoring the performance of Global Satellite Navigation Systems (GNSS). Integrity assessments for safety-critical applications demand the knowledge of possible faults, frequency of occurrence, their precise analysis and modeling. The risk of GNSS signal failures and anomalies play a significant role. Within the context of augmentation, monitoring schemes for signal threat detection already exist, e.g., in the Wide Area Augmentation System (WAAS) and the European Geostationary Navigation Overlay Service (EGNOS). However, such systems have been deployed in order to monitor application-specific metrics and therefore are limited in the scope of detection of signal changes. In addition, the detection capabilities are limited to a certain area, e.g., North America and Europe/Africa. This study aims at the development of a monitoring system for a global and continuous search for general GNSS signal changing events, which do not necessarily represent a threat for a specific application. Given the intended functionality to monitor all GNSS satellites, the developed system makes use of the publicly, continuously-operated and globally-distributed data from the International GNSS Service (IGS). Previous studies have shown the usability of differential code bias (DCB) estimates in combination with classical and machine learning methods to detect anomalies in GNSS signals. In this study, DCB estimates with 2-h sampling are used as input data for machine learning algorithms for the detection of anomalous signatures, considering strategies for special cases such as the impact of flex power modes of GPS IIR-M/IIF satellites. The analysis is based on historical data from the last 10 years. The obtained results are employed in the elaboration of a catalogue of signal anomaly events for all the global GNSS (GPS, Galileo, GLONASS and BDS-3), providing information about the approximate time and duration of occurrence of each event. The present analysis aims at contributing to current efforts of international and standardization groups dedicated to the assessment of GNSS integrity risks associated to signal faults for safety-critical applications.



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