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Magnetic Field Gradient-Based EKF for Velocity Estimation in Indoor Navigation

Makia Zmitri 1, 2 Hassen Fourati 2 Christophe Prieur 1, 3
1 GIPSA-INFINITY - GIPSA - Infinite Dimensional Dynamics
GIPSA-PAD - GIPSA Pôle Automatique et Diagnostic
2 DANCE - Dynamics and Control of Networks
Inria Grenoble - Rhône-Alpes, GIPSA-PAD - GIPSA Pôle Automatique et Diagnostic
3 TRIPOP - Modélisation, simulation et commande des systèmes dynamiques non lisses
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann
Abstract : This paper proposes an advanced solution to improve the inertial velocity estimation of a rigid body, for indoor navigation, through implementing a magnetic field gradient-based Extended Kalman Filter (EKF). The proposed estimation scheme considers a set of data from a triad of inertial sensors (accelerometer and gyroscope), as well as a determined arrangement of magnetometers array. The inputs for the estimation scheme are the spatial derivatives of the magnetic field, from the magnetometers array, and the attitude, from the inertial sensors. As it was shown in the literature, there is a strong relation between the velocity and the measured magnetic field gradient. However, the latter usually suffers from high noises. Then, the novelty of the proposed EKF is to develop a specific equation to describe the dynamics of the magnetic field gradient. This contribution helps to filter, first, the magnetic field and its gradient and second, to better estimate the inertial velocity. Some numerical simulations that are based on an open source database show the targeted improvements. At the end of the paper, this approach is extended to position estimation in the case of a foot-mounted application and the results are very promising.
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Submitted on : Thursday, February 11, 2021 - 10:22:54 AM
Last modification on : Wednesday, November 3, 2021 - 6:03:30 AM
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Makia Zmitri, Hassen Fourati, Christophe Prieur. Magnetic Field Gradient-Based EKF for Velocity Estimation in Indoor Navigation. Sensors, MDPI, 2020, 20 (20), pp.1-17. ⟨10.3390/s20205726⟩. ⟨hal-03138331⟩



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