A Machine Learning Framework for Weighted Least Squares GNSS Positioning based on Activation Functions
arXiv:2605.21461v1 Announce Type: new Abstract: Global Navigation Satellite Systems (GNSS) are widely used to provide position, velocity, and timing (PVT) information for various applications, including transportation, location-based communication services, and intelligent agriculture. In urban canyons, high-rise buildings and narrow streets can cause signal obstruction, non-line-of-sight (NLOS) reception, and multipath effects that introduce errors in GNSS pseudorange measurements. Although multi-constellations GNSS effectively increase the number of available satellites, the inclusion of deg
The continuous evolution of GNSS technology and the increasing reliance on precise positioning in complex environments drive the need for advanced error mitigation techniques, making AI/ML integration timely.
Improving the accuracy and reliability of GNSS positioning, especially in challenging urban environments, is crucial for numerous applications, enhancing the robustness of location-based services and autonomous systems.
The application of machine learning to weighted least squares GNSS positioning offers a novel approach to significantly reduce errors caused by signal obstruction and multipath effects, potentially making GNSS more dependable.
- · Autonomous vehicle developers
- · Logistics and transportation sectors
- · Location-based service providers
- · AI/ML researchers
- · Traditional GNSS error correction methods
- · Systems heavily reliant on uncorrected GNSS in urban areas
More accurate and reliable positioning data becomes available for various applications, particularly in urban canyons.
This improved accuracy can accelerate the development and deployment of autonomous systems that depend on precise location.
Enhanced GNSS reliability could reduce the need for expensive supplementary positioning technologies in some contexts, influencing technological integration strategies.
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