mlsanityData to 14 Oct 2026

NavFormer: IGRF Forecasting in Moving Coordinate Frames

arXiv 2601.18800 ↗First posted 14 Jan 2026cs.LG cs.CVcited by 0

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Abstract (arXiv)

Triad magnetometer components change with sensor attitude even when the IGRF total intensity target stays invariant. NavFormer forecasts this invariant target with rotation invariant scalar features and a Canonical SPD module that stabilizes the spectrum of window level second moments of the triads without sign discontinuities. The module builds a canonical frame from a Gram matrix per window and applies state dependent spectral scaling in the original coordinates. Experiments across five flights show lower error than strong baselines in standard training, few shot training, and zero shot transfer. The code is available at: https://anonymous.4open.science/r/NavFormer-Robust-IGRF-Forecasting-for-Autonomous-Navigators-0765

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 35 references that are in the map. First posted on arXiv 14 Jan 2026.

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17 of 35 references.

References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  5. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  6. 2016Group Equivariant Convolutional Networkscited by 1,165
  7. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  8. 2021E(n) Equivariant Graph Neural Networkscited by 965
  9. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  10. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 607
  11. 2022Transformers in Time Series: A Surveycited by 497
  12. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 168
5 more
  1. 2016A Riemannian Network for SPD Matrix Learningcited by 162
  2. 2017Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalizationcited by 65
  3. 2025TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecastingcited by 64
  4. 2020Signal Enhancement for Magnetic Navigation Challenge Problemcited by 5
  5. 2024Physics-Informed Calibration of Aeromagnetic Compensation in Magnetic Navigation Systems using Liquid Time-Constant Networkscited by 2

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