Resilient PNT for Urban and Cislunar Environments: GNSS, Cooperative Constellations and AI Fusion

Dear Colleagues,

Global Navigation Satellite Systems (GNSSs) are foundational to positioning, navigation, and timing (PNT) across platforms ranging from unmanned aerial vehicles (UAVs) and ground vehicles to cislunar spacecraft. However, signal blockage, multipath, jamming in urban canyons, and the absence of navigation infrastructure in cislunar space critically undermine reliability. This Topic solicits contributions addressing resilient PNT through three converging technological pillars: (1) cooperative and networked GNSS techniques, leveraging inter-agent ranging, V2X communication, and IoT-enabled crowdsourcing to enhance availability and robustness; (2) augmented navigation constellations, including LEO mega-constellations and emerging lunar navigation infrastructures (e.g., LunaNet) that complement traditional GNSS with improved geometry and extended coverage; and (3) AI-enhanced multi-sensor fusion, integrating machine learning with IMUs, LiDAR, vision, and satellite signals for adaptive, context-aware PNT across heterogeneous environments. We welcome submissions spanning urban canyon positioning, UAV navigation under GNSS-degraded conditions, cislunar navigation concepts, multi-constellation signal processing, integrity monitoring, resilient timing, and hardware–software co-design for next-generation receivers. Drawing researchers from satellite navigation, remote sensing, aerospace, electronics, drone technology, and networked communications, this Topic advances both theoretical foundations and practical implementations for reliable PNT where it is most critical yet most challenging.

Prof. Dr. Ying Xu
Dr. Rui Sun
Dr. Jiajia Chen
Dr. Ming Gao
Topic Editors