Magnetic-Field-Based Localization Using Spatial Field Variations: Signal Processing Principles, Models, and Challenges
arXiv:2602.14181
The paper surveys signal‑processing methods for indoor and outdoor localization that exploit spatial variations in the Earth's magnetic field, covering modeling, state inference, and sensor calibration.
Abstract
Signal processing has played, and continues to play, a fundamental role in the evolution of modern localization technologies. Localization using spatial variations in the Earth's magnetic field is no exception. It relies on signal-processing methods for statistical state inference, magnetic-field modeling, and sensor calibration. Contemporary localization techniques based on spatial variations in the magnetic field can provide decimeter-level indoor localization accuracy and outdoor localization accuracy on par with strategic-grade inertial navigation systems. This article provides a broad, high-level overview of current signal-processing principles and open research challenges in localization using spatial variations in the Earth's magnetic field. The aim is to provide the reader with an understanding of the similarities and differences among existing key technologies from a statistical signal-processing perspective. To that end, existing key technologies will be presented within a common parametric signal-model framework compatible with well-established statistical inference methods.
This work has been submitted to the IEEE for possible publication