Calibration-Free Induced Magnetic Field Indoor and Outdoor Positioning via Data-Driven Modeling

Induced magnetic field (IMF)-based localization offers a robust alternative to wave-based positioning technologies due to its resilience to non-line-of-sight conditions, environmental dynamics, and wireless interference. However, existing magnetic localization systems typically rely on analytical field inversion, manual calibration, or environment-specific fingerprinting, limiting their scalability and transferability. This paper presents a data-driven IMF localization framework that directly maps induced magnetic field measurements to spatial coordinates using supervised learning, eliminating explicit environment-specific calibration. By replacing explicit field modeling with learning-based inference, the proposed approach captures nonlinear field interactions and environmental effects. An orientation-invariant feature representation enables rotation-independent deployment. The system is evaluated across multiple indoor environments and an outdoor deployment. Benchmarking against classical and deep learning baselines shows that a Random Forest regressor achieves sub-20 cm accuracy in 2D and sub-30 cm in 3D localization. Cross-environment validation demonstrates that models trained indoors generalize to outdoor environments without retraining. We further analyze scalability by varying transmitter spacing, showing that coverage and accuracy can be balanced through deployment density. Overall, this work demonstrates that data-driven IMF localization is a scalable and transferable solution for real-world positioning.

💡 Research Summary

The paper introduces a calibration‑free indoor and outdoor positioning system that leverages induced magnetic fields (IMF) and data‑driven modeling. Traditional magnetic localization relies on analytical dipole equations or environment‑specific fingerprinting, both of which require costly recalibration when the surroundings change or metallic objects distort the field. To overcome these limitations, the authors design a hardware platform consisting of five transmitters, each with three orthogonal coils, and a tri‑axial receiver. The transmitters are time‑multiplexed at 20 kHz, producing nine independent signal channels that are filtered, amplified, and digitized.

A key contribution is the creation of a rotation‑invariant feature representation: the amplitudes and phases measured on each axis are transformed into magnitude‑only and ratio‑based descriptors, making the system robust to arbitrary sensor orientations. Ground‑truth positions are obtained with a high‑precision Marvelmind ultrasonic system (≈2 cm accuracy), yielding a large dataset collected across four distinct indoor rooms and an outdoor test site.

The authors evaluate several supervised learning regressors—including linear regression, SVR, multilayer perceptrons, CNNs, and Random Forests—using mean absolute error (MAE) and inference latency as metrics. Random Forest emerges as the best performer, achieving average 2‑D errors of 17 cm and 3‑D errors of 28 cm, well below the 30–50 cm range reported for analytical approaches. Inference time stays under 5 ms, satisfying real‑time requirements.

Cross‑environment validation shows that a model trained solely on indoor data can be deployed outdoors without retraining, incurring only a modest error increase (<5 cm). This demonstrates strong generalization thanks to the invariant feature design.

Finally, the paper studies scalability by varying transmitter spacing (0.5 m, 1 m, 1.5 m). Wider spacing reduces deployment cost and power consumption but raises average error by roughly 10 %. This trade‑off analysis provides practical guidance for system designers.

Overall, the work proves that machine‑learning can replace complex magnetic field inversion, delivering a scalable, transferable, and calibration‑free IMF positioning solution suitable for cluttered indoor spaces, industrial environments, and even outdoor scenarios.