Accurate Orientation Estimation Using AHRS under Conditions of Magnetic Distortion

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Title: Accurate Orientation Estimation Using AHRS under Conditions of Magnetic Distortion
Authors: Yadav, Nagesh
Bleakley, Chris J.
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Date: 24-Oct-2014
Abstract: Low cost, compact attitude heading reference systems (AHRS) are now being used to track human body movements in indoor environments by estimation of the 3D orientation of body segments. In many of these systems, heading estimation is achieved by monitoring the strength of the Earth’s magnetic field. However, the Earth’s magnetic field can be locally distorted due to the proximity of ferrous and/or magnetic objects. Herein, we propose a novel method for accurate 3D orientation estimation using an AHRS, comprised of an accelerometer, gyroscope and magnetometer, under conditions of magnetic field distortion. The system performs online detection and compensation for magnetic disturbances, due to, for example, the presence of ferrous objects. The magnetic distortions are detected by exploiting variations in magnetic dip angle, relative to the gravity vector, and in magnetic strength. We investigate and show the advantages of using both magnetic strength and magnetic dip angle for detecting the presence of magnetic distortions. The correction method is based on a particle filter, which performs the correction using an adaptive cost function and by adapting the variance during particle resampling, so as to place more emphasis on the results of dead reckoning of the gyroscope measurements and less on the magnetometer readings. The proposed method was tested in an indoor environment in the presence of various magnetic distortions and under various accelerations (up to 3 g). In the experiments, the proposed algorithm achieves <2° static peak-to-peak error and <5° dynamic peak-to-peak error, significantly outperforming previous methods.
Type of material: Journal Article
Publisher: MDPI
Keywords: AHRSParticle filterData fusionInertial measurement unitsMagnetic distortionMotion capture
DOI: 10.3390/s141120008
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection
CASL Research Collection

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