Magnetic-Field-Based Localization Using Spatial Field Variations: Signal Processing Principles, Models, and Challenges

TL;DR

This paper reviews signal processing principles and models for magnetic-field-based localization, highlighting its potential for high-accuracy indoor and outdoor positioning and discussing current challenges.

Contribution

It provides a comprehensive overview of existing magnetic localization techniques within a unified signal-model framework from a statistical perspective.

Findings

Magnetic localization can achieve decimeter-level indoor accuracy.

Current techniques are comparable to inertial navigation systems outdoors.

The paper identifies open research challenges in the field.

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.