A Robust Orientation Estimation Algorithm Using MARG Sensors

This paper presents a robust orientation estimation algorithm by using magnetic, angular rate, and gravity sensors. The robustness is achieved by two online methods: compensation of hard iron effect for the magnetometer and separation of the accelerometer signals into gravity projections and linear...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 64; no. 3; pp. 815 - 822
Main Authors: Hu, Jwu-Sheng, Sun, Kuan-Chun
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
9-D inertial measurement unit (IMU)
Acceleration
and gravity (MARG) sensors
angular rate
direct cosine matrix (DCM)
Estimation
Gravity
gravity projection
Iron
magnetic
Magnetic separation
Magnetometers
orientation estimation
real-time hard iron compensation
sensor fusion
state-constrained Kalman filter
Vectors
and gravity (MARG) sensors
orientation estimation
9-D inertial measurement unit (IMU)
state-constrained Kalman filter
gravity projection
magnetic
sensor fusion
real-time hard iron compensation
angular rate
direct cosine matrix (DCM)
ISSN:0018-9456, 1557-9662
Online Access:Get full text
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Summary:This paper presents a robust orientation estimation algorithm by using magnetic, angular rate, and gravity sensors. The robustness is achieved by two online methods: compensation of hard iron effect for the magnetometer and separation of the accelerometer signals into gravity projections and linear accelerations. Further, direct cosine matrix is used for the rotation matrix relative to the north, east, down frame. The fusion equations are solved using state-constrained Kalman filter. The simulation and experiment results show the effectiveness of the proposed algorithm in estimating the orientation under acceleration and hard iron influence.
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content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2014.2359815