Location-based services, safety-critical applications, and modern intelligent transportation systems require reliable, continuous and precise positioning, navigation and timing (PNT) information. Global Navigation Satellite Systems (GNSS) is the main source of positioning data in open sky conditions, however its vulnerabilities to radio interferences and signal propagation limits its use in challenging environments. As a consequence, enhancing conventional GNSS-based PNT solutions to account for additional sensing modalities and exploiting other available signals of opportunity has become a necessity for continuous and reliable navigation. The goal of this special issue is to compile the latest advances in PNT solutions, with strong emphasis on those methods leveraging novel statistical signal processing and machine learning methods. These advances address technical challenges which are motivated by PNT-based applications for contested environments. For instance, relevant scenarios include navigating interference-rich areas (that is, under jamming or spoofing attacks); weak signal or multipath-rich propagation conditions, including indoor navigation but also new explorative environments such as lunar or underwater navigation. In this context, this special issue calls for contributed articles on novel methodologies for PNT algorithms with solid foundations in statistical signal processing and machine learning, in topics such as data fusion, spatio-temporal filtering, distributed learning and cooperative positioning, as well as the interplay between physics and data-driven modeling for PNT solutions.
Topics of interest include but are not limited to:
- Interference and spoofing detection and mitigation
- GNSS anomaly detection and multipath mitigation
- Indoor/outdoor seamless PNT
- LEO-based PNT and GNSS augmentation
- PNT solutions for challenging environments (underwater, underground, or space exploration)
- Centralized and distributed PNT (including cooperative positioning and swarms navigation)
- Machine learning methods for PNT algorithms
- Physics-guided machine learning techniques for PNT Experimental testbeds
Submission Status: Closed | Submission Deadline: Closed
This collection is no longer accepting submissions
Lead Guest Editor
Pau Closas, Northeastern University, USA
closas@northeastern.edu
Guest Editors
Petar M. Djuric, Stony Brook University, USA
Lorenzo Ortega, University of Toulouse, France
Julien Lesouple, University of Toulouse, France
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