Topic Editors

Prof. Dr. Ying Xu
Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Dr. Jiajia Chen
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Dr. Ming Gao
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

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

Abstract submission deadline
30 June 2027
Manuscript submission deadline
30 September 2027
Viewed by
204

Topic Information

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

Keywords

  • resilient PNT
  • urban navigation
  • cislunar navigation
  • cooperative GNSS
  • LEO mega-constellations
  • AI-enhanced fusion
  • multi-sensor integration

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Aerospace
aerospace
2.2 4.8 2014 22.9 Days CHF 2400 Submit
Drones
drones
4.8 10.0 2017 20.8 Days CHF 2600 Submit
Future Internet
futureinternet
3.6 10.0 2009 16.1 Days CHF 1800 Submit
Remote Sensing
remotesensing
4.1 9.4 2009 24.3 Days CHF 2700 Submit
Sensors
sensors
3.5 9.4 2001 17.8 Days CHF 2600 Submit

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Published Papers (1 paper)

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33 pages, 3890 KB  
Article
Robust Spatial Georeferencing for UAV-UGV Mobile Mapping Platforms in Urban Canyons via Asymmetric GNSS/UWB Fusion
by Jiajia Chen, Xing’ao Wang, Zhibo Fang, Ming Gao, Ying Xu and Zhiyou Zhang
Remote Sens. 2026, 18(12), 1967; https://doi.org/10.3390/rs18121967 (registering DOI) - 13 Jun 2026
Abstract
Reliable spatial georeferencing of mobile mapping platforms is a fundamental prerequisite for high-fidelity urban remote sensing products such as 3D point clouds and digital twins. However, in deep urban canyons, severe signal occlusion and multipath effects reduce visible GNSS satellites, causing ambiguity resolution [...] Read more.
Reliable spatial georeferencing of mobile mapping platforms is a fundamental prerequisite for high-fidelity urban remote sensing products such as 3D point clouds and digital twins. However, in deep urban canyons, severe signal occlusion and multipath effects reduce visible GNSS satellites, causing ambiguity resolution (AR) failure and degraded observation geometry for UGV-borne systems. Conventional Vehicle-to-Vehicle (V2V) cooperation offers limited improvement due to symmetric ground-level occlusion. To overcome this, we propose an asymmetric GNSS/UWB fusion method that introduces Unmanned Aerial Vehicles (UAVs) as high-altitude dynamic spatial anchors to reconstruct the 3D observation geometry. Two contributions are presented: (i) an asymmetric heterogeneous stochastic model coupling carrier-to-noise ratio (C/N0) and elevation angle to handle the quality disparity between air and ground sensor links, preventing multipath contamination of high-fidelity UAV observations; and (ii) a dynamic baseline constrained least-squares algorithm integrating Ultra-Wideband (UWB) ranging to stabilize GNSS positioning under high-dynamic relative motion. Validated through high-fidelity simulations and field experiments, the method achieves a 98.2% AR success rate and sub-decimeter 3D accuracy under extreme occlusion (≤3 visible satellites), while urban-canyon tests demonstrate 100% positioning availability across all evaluated epochs and reduce the 95th-percentile 3D error from 7.25 m to 0.19 m under the tested single-UAV/single-UGV configuration. The framework supports smart city modeling, 3D reconstruction, and infrastructure monitoring. Full article
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