AI & ML
FlightMagNav: An Open Dataset and Probabilistic Map Learning and Validation Framework for Outdoor Magnetic Field-Based Positioning
arXiv eess.SP0 views
arXiv:2610.08982v1 Announce Type: new Abstract: Magnetic field-based positioning is a resilient positioning technology that requires no external infrastructure and is hard to jam at scale. To facilitate research on magnetic field-based positioning for aerial platforms operating close to the Earth's surface, an open dataset is presented with measurements collected using an unmanned aerial vehicle carrying two optically pumped magnetometers, a type of quantum magnetometer, and a global navigation satellite system-aided inertial navigation system. The dataset includes measurements for both magnetic-field map learning and validation. Along with the dataset, a probabilistic framework for magnetic-field map learning and validation is presented and used to illustrate how the dataset may be used. Finally, we outline research directions that may be explored using the dataset.
Source:https://arxiv.org/abs/2610.08982PreviousBeamforming Design for RIS-Assisted Backscatter NOMA in Underlay Cognitive RadioNextGeometry-Informed Neural Network for Pilot-Limited Channel Estimation via Sensing from Uplink Traffic
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