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.
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