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A moving charge or a current creates a magnetic field in the surrounding space, in addition to its electric field. The magnetic field exerts a force on any other moving charge or current that is present in the field. Like an electric field, the magnetic field is also a vector field. At any position, the direction of the magnetic field is defined as the direction in which the north pole of a compass needle points.
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Magnetic Field Gradient-Based EKF for Velocity Estimation in Indoor Navigation.

Makia Zmitri1, Hassen Fourati1, Christophe Prieur1

  • 1Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, F-38000 Grenoble, France.

Sensors (Basel, Switzerland)
|October 14, 2020
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Summary

This study enhances indoor navigation by using a magnetic field gradient-based Extended Kalman Filter (EKF) to improve inertial velocity estimation. The novel EKF approach filters magnetic field noise for more accurate velocity and position tracking.

Keywords:
Extended Kalman Filterindoor navigationinertial velocity estimationmagnetic field gradientspatial derivatives

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Area of Science:

  • Robotics
  • Navigation Systems
  • Sensor Fusion

Background:

  • Inertial navigation systems (INS) are crucial for indoor navigation but suffer from drift.
  • Accurate velocity estimation is essential for mitigating INS drift.
  • Magnetic field gradients offer a potential velocity-dependent measurement but are susceptible to noise.

Purpose of the Study:

  • To propose an advanced solution for improving inertial velocity estimation in rigid bodies for indoor navigation.
  • To implement a magnetic field gradient-based Extended Kalman Filter (EKF) for enhanced estimation accuracy.
  • To develop a novel dynamic model for magnetic field gradients to improve filtering and velocity estimation.

Main Methods:

  • Utilized a triad of inertial sensors (accelerometer and gyroscope) for attitude and motion data.
  • Employed a magnetometer array to measure spatial derivatives of the magnetic field.
  • Developed a specific equation to model the dynamics of the magnetic field gradient within an EKF framework.
  • Integrated inertial sensor data with magnetic field gradient measurements for state estimation.

Main Results:

  • Demonstrated significant improvements in inertial velocity estimation accuracy through numerical simulations.
  • Successfully filtered noisy magnetic field and gradient data.
  • Showcased the effectiveness of the proposed EKF in a foot-mounted indoor navigation application.
  • Achieved promising results for position estimation extending the velocity estimation approach.

Conclusions:

  • The proposed magnetic field gradient-based EKF effectively enhances inertial velocity estimation for indoor navigation.
  • The novel dynamic model for magnetic field gradients improves the filtering of noisy measurements.
  • The approach shows potential for accurate position estimation in applications like foot-mounted INS.