Geomagnetic Localization with Neural Networks: A Comparative Survey

Geomagnetic positioning, in principle, enables infrastructure-free indoor localization—provided that spatial variations in the magnetic field can be distinguished with sufficient resolution. Neural networks represent a powerful model class for recognizing complex spatio-temporal patterns in the magnetic signal landscape.This paper presents a systematic survey of research that employs neural algorithms to process geomagnetic measurements for the purpose of positioning. Feature representations, model architectures, ground-truth strategies, and multimodal fusion approaches are compared. A comprehensive tabular overview allows direct comparison of the employed data sets, feature sets, algorithms, and the operationalization of both the reference and localization phases.

• Publication year

  2025

• Venue

  International Conference on Indoor Positioning and Indoor Navigation

• Publication date

  2025-09-15

• Fields of study

  Physics, Computer Science, Engineering

• Identifiers
  DOI 10.1109/IPIN66788.2025.11212897
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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