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Related Experiment Video

Updated: Feb 28, 2026

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Millimeter-Scale Magnetic Positioning Using a Single AMR Sensor and BP Neural Network.

Guanjun Zhang1, Zihe Zhao1, Peiwen Luo1

  • 1National Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 611731, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a single-sensor magnetic positioning system using an anisotropic magnetoresistive (AMR) sensor and a BP neural network. It achieves millimeter-level accuracy for real-time tracking, reducing hardware costs and system complexity.

Keywords:
back propagation neural networkmagnetic field positioningmagnetic fields sensor

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Area of Science:

  • Robotics and Automation
  • Sensor Technology
  • Machine Learning Applications

Background:

  • Conventional positioning systems often require multiple sensors, increasing cost and complexity.
  • Miniaturization of positioning systems is crucial for applications with space constraints.
  • Accurate real-time tracking is essential in various advanced technological fields.

Purpose of the Study:

  • To develop a cost-effective and miniaturized positioning system using a single sensor.
  • To achieve high-precision real-time tracking and localization via magnetic field measurement.
  • To explore the application of BP neural networks for magnetic field data conversion.

Main Methods:

  • Utilized a single anisotropic magnetoresistive (AMR) sensor to detect the magnetic field of a target permanent magnet.
  • Employed a Backpropagation (BP) neural network to process three-axis magnetic field data.
  • Converted magnetic field data into precise coordinate information for localization.

Main Results:

  • Achieved millimeter-level accuracy in simulations (RMSEs of 0.27 mm for X, 0.26 mm for Z) and real-world tests (RMSEs of 0.83 mm for X, 1.15 mm for Y, 0.85 mm for Z).
  • Demonstrated positioning error correlation with magnetic field variations due to distance-dependent nonlinearity.
  • Successfully tracked and localized within a 50 × 40 × 40 mm³ volume.

Conclusions:

  • The single-sensor AMR system offers a significant reduction in hardware cost and complexity compared to multi-sensor systems.
  • The BP neural network effectively converts magnetic field data to achieve accurate, real-time positioning.
  • This method is suitable for high-precision applications in aerospace, medical devices, and automation where space is limited.