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An applied magnetic field causes loosely bound π-electrons in organic molecules to circulate, producing a local or induced diamagnetic field over a large spatial volume. As the molecules tumble in solution, the field generated by π-electrons in spherical substituents results in a zero net field. However, the net field generated by π-electrons in non-spherical substituents is not zero. The effect of this induced field depends on the orientation of the molecule with respect to B0, resulting in...
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Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
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Published on: June 9, 2016

Magnetic Heterodyne Target Proximal Distance Estimate Using Extended N-th-Pole Magnetic Dipole Model via Iterative

Xuyi Miao1, Yipeng Li1, Zumeng Jiang1

  • 1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

This study introduces advanced magnetic anomaly detection models for all-weather anti-collision systems. The Extended N-th-Pole Magnetic Dipole (E-NMD) model and adaptive filters significantly improve accuracy and robustness in challenging conditions.

Keywords:
Kalman Filtermagnetic anomaly detectionmagnetic dipole model

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Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

Published on: November 7, 2017

Area of Science:

  • Geophysics
  • Sensor Technology
  • Robotics

Background:

  • Conventional anti-collision sensors (optical, radar, laser) fail in adverse weather.
  • Magnetic anomaly detection offers all-weather capability but traditional models lack accuracy.
  • Existing methods struggle with short-range targets and parameter uncertainty.

Purpose of the Study:

  • To enhance magnetic anomaly detection for improved anti-collision systems.
  • To develop a more accurate magnetic dipole model for proximal sensing.
  • To create robust algorithms for state and parameter estimation under noise and uncertainty.

Main Methods:

  • Proposed an Extended N-th-Pole Magnetic Dipole (E-NMD) model with Lagrangian cosine term analysis.
  • Developed an Adaptive Iterative Extended Kalman Filter (AI-EKF) for noise suppression and distance estimation.
  • Introduced a Dual-Mode Pairwise Iterative Extended Kalman Filter (DI-EKF) for joint state and parameter estimation.

Main Results:

  • E-NMD model achieved 99.87% fitting variance for steel.
  • E-NMD model reduced Root Mean Square Error (RMSE) by 39.62% in proximal state estimation compared to traditional NMD.
  • DI-EKF yielded an 89% reduction in RMSE compared to AI-EKF for parameter uncertainty.

Conclusions:

  • The E-NMD model and AI-EKF significantly improve magnetic anomaly detection accuracy and robustness.
  • DI-EKF effectively addresses parameter uncertainty in real-world magnetic anomaly detection.
  • These advancements enable more reliable all-weather anti-collision systems.