Advancement of GNSS Signal Processing and Navigation

Dear Colleagues,

Global navigation satellite systems (GNSS) are evolving quickly towards offering precise position, navigation, and timing (PNT) services via modernized signals and new frequencies. In addition, there are more and more satellites being launched from different global and regional constellations, helping to improve navigation performance by allowing the user–receiver to utilize multi-GNSS and multi-frequency diversity. This Special Issue will address novel GNSS technologies that exploit these new signals and new features, for example, Galileo Open Service Navigation Message Authentication (OSNMA), which is in its public observation phase, Galileo High Accuracy Service (HAS), which is in its test and experimentation phase, or other correction services, for example, the wide area differential correction for BeDiou Satellite Navigation system (BDS).

This Special Issue will investigate different techniques and solutions that can improve receiver performance through advanced algorithms at signal processing and navigation layers and/or in conjunction with different state-of-the-art sensors adopted in different user case scenarios in road, air, and maritime transport modes.

This Special Issue invites contributions on the following topics (but is not strictly limited to these subject areas):

• Advances in multi-frequency multi-constellation (MFMC) GNSS signal processing;
• Resilient and robust position, navigation, and timing (PNT);
• Advances in precise point positioning (PPP);
• Advances in real-time kinematic (RTK) positioning;
• Advances in signal processing techniques for high-precision applications addressing the requirements for autonomous navigation in all transport mode;
• GNSS vulnerability detection, characterization, and mitigation in challenging environments;
• Sensor fusion for robust high-precision applications;
• Cloud-based solutions for PNT;
• Alternative or dedicated PNT with low earth orbit (LEO) satellites;
• Sensor fusion with signals of opportunity (SoOP) for robust and accurate PNT.

Prof. Mohammad Zahidul Hasan Bhuiyan
Prof. Dr. Elena Simona Lohan
Guest Editors

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