GNSS
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Related Articles from SNS
GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series
arXiv:2606.07725v1 Announce Type: new Abstract: Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and investigating the different stages of the earthquake cycle. Machine learning methods have proven promising for GNSS applications; however, most remain fully supervised. This creates a bottleneck as labeled data are scarce, even though large amounts of unlabeled GNSS data...
GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series
arXiv:2606.07725v1 Announce Type: cross Abstract: Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and investigating the different stages of the earthquake cycle. Machine learning methods have proven promising for GNSS applications; however, most remain fully supervised. This creates a bottleneck as labeled data are scarce, even though large amounts of unlabeled GNSS...
Tracing a powerful GNSS interference source over Europe
Electrical Engineering and Systems Science > Signal Processing [Submitted on 2 Jun 2026] Title:Chasing Lightning: Detecting, Characterizing, and Identifying a Powerful Space-Based GNSS Interference Source View PDF HTML (experimental)Abstract:This paper analyzes and identifies a space-based Global Navigation Satellite System (GNSS) interference source that has caused scores of powerful transient wide-area interference events over continental Europe, Greenland, and Canada since 2019.
LdT: An indicator of ionospheric activity based on statistical distributions in GNSS-derived TEC rates of change
arXiv:2504.06056v3 Announce Type: replace Abstract: Many aspects of our societies now depend upon satellite telecommunications, such as those requiring Global Navigation Satellite Systems (GNSS). GNSS is based on radio waves that propagate through the ionosphere and experience complicated propagation effects caused by inhomogeneities in its electron density. The Earth's ionosphere forms part of the solar-terrestrial environment, and its state is determined by the spatial distribution and...
Impact of RTK Augmentation and INS Integration on GNSS Positioning Accuracy and Continuity: A Benchmarking Study on Inland Waterways
arXiv:2606.06358v1 Announce Type: new Abstract: RTK augmentation andINS integration are widely used to improve GNSS positioning performance. However, on inland waterways, bridges and surrounding structures can degrade satellite visibility and correction availability, causing RTK augmentation loss, and GNSS/INS fusion transients.
MineXplore: An Open-Source Reinforcement Learning Exploration Benchmark for GNSS-Denied Underground Environment
Announce Type: new Abstract: Underground mines present extreme conditions for autonomous robot navigation: GPS is denied, lighting is degraded, and tunnel topology is loop-rich and non-convex. Simulation benchmarks grounded in real production-mine geometry and compatible with GPU-accelerated learning pipelines do not yet exist in the open-source ecosystem. We present MineXplore, an open-source MuJoCo-based navigation benchmark derived from the Leung et al. 2017
Seq-DeepIPC: Sequential Sensing for End-to-End Control in Legged Robot Navigation
arXiv:2510.23057v2 Announce Type: replace Abstract: We present Seq-DeepIPC, a sequential end-to-end perception-to-control model for legged robot navigation in real-world environments. Seq-DeepIPC advances intelligent sensing for autonomous legged navigation by tightly integrating multi-modal perception (RGB-D + GNSS) with temporal fusion and control. The model jointly predicts semantic segmentation and depth estimation, giving richer spatial features for planning and control.
Wheel-Mounted/GNSS Fusion with AI-Aided Position Updates
Announce Type: new Abstract: Accurate and robust localization remains a fundamental challenge for autonomous ground vehicles. In this work, we propose a hybrid neural inertial navigation framework that integrates a wheel-mounted inertial sensors, enforced periodic trajectories, and a simple, efficient neural network capable of regressing vehicle displacement with GNSS position updates in an error-state extended Kalman filter. The periodic trajectories increase the inertial signal-to-noise...
Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments
Announce Type: replace Abstract: Autonomous unmanned aerial vehicles (UAVs) increasingly rely on reinforcement learning (RL) for navigation. However, global navigation satellite system (GNSS) spoofing attacks can induce out-of-distribution observation shifts that corrupt value estimation and degrade mission performance. Existing robust RL approaches typically improve resilience against specific attack models but often fail to generalize to attacks not encountered during training.
Meridian: Metric-Semantic Primitive Matching for Cross-View Geo-Localization Beyond Urban Environments
Announce Type: new Abstract: Successful robot automation requires accurate global localization to support repeatability, task planning, goal specification, and safe operation. However, reliable localization in GNSS-denied environments remains an open problem. Overhead aerial imagery offers a promising solution, but existing approaches primarily target structured urban environments and have been rarely demonstrated in unstructured natural terrain.