Design and implementation of an Attitude and Heading Reference System (AHRS)

Guerrero-Castellanos, J.F.; Madrigal-Sastre, H.; Durand, Sylvain; Marchand, Nicolas; Guerrero-Sánchez, W. F.; Salmeron, B.B.

doi:10.1109/iceee.2011.6106610

Cited by 19 publications

(7 citation statements)

References 13 publications

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“…Apart from that, the system demonstrated dependency to methods of initialization. Adaptive Kalman filter (AKF) [6], extended Kalman filter (EKF) [7], and dual Kalman filter (DKF) methods [8] have been performed for orientation estimation with a basic model of the system. A precise stable Kalman-based AHRS was designed as an approach of the simultaneous localization and mapping (SLAM) applications by the authors of [9].…”

Section: Related Workmentioning

confidence: 99%

Accuracy Improvement of Attitude Determination Systems Using EKF-Based Error Prediction Filter and PI Controller

Farhangian

Landry

2020

Sensors

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Accurate attitude and heading reference system (AHRS) play an essential role in navigation applications and human body tracking systems. Using low-cost microelectromechanical system (MEMS) inertial sensors and having accurate orientation estimation, simultaneously, needs optimum orientation methods and algorithms. The error of attitude estimation may lead to imprecise navigation and motion capture results. This paper proposed a novel intermittent calibration technique for MEMS-based AHRS using error prediction and compensation filter. The method, inspired from the recognition of gyroscope’s error and by a proportional integral (PI) controller, can be regulated to increase the accuracy of the prediction. The experimentation of this study for the AHRS algorithm, aided by the proposed prediction filter, was tested with real low-cost MEMS sensors consists of accelerometer, gyroscope, and magnetometer. Eventually, the error compensation was performed by post-processing the measurements of static and dynamic tests. The experimental results present about 35% accuracy improvement in attitude estimation and demonstrate the explicit performance of proposed method.

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Section: Related Workmentioning

confidence: 99%

Accuracy Improvement of Attitude Determination Systems Using EKF-Based Error Prediction Filter and PI Controller

Farhangian

Landry

2020

Sensors

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“…Fig. 3 Diagram of AHRS The basic step of AHRS could be described as in paper [6,7] USV Mathematical Model.USV mathematical model was need for control. The USV movement can be represented by the following equation [8].…”

Section: Design Of the Usv Experiments Platformmentioning

confidence: 99%

Design of Unmanned Surface Vessels Experiment Platform

Wang¹

2016

Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)

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In recent years, More and more attention has been paid to Unmanned Surface Vessels motion control field. An experimental platform for USV based on wireless communication was introduced for USV motion control algorithm experiments in this paper. The structure and implementation of experimental platform was described in detail. Stability and reliability tests for experimental platform were carried out by following an circular path, and the USV state data in sailing was analyzed by drawing tools in MATLAB. Experimental results show that the platform can complete algorithm validation experiments accurately.

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“…In order to employ a non-singular attitude representation in the estimator, MEKFs formulate the attitude as a multiplication of an estimated attitude quaternion and an error quaternion representing the deviation between the above estimates and true attitude. Due to the advantages of the non-singularity representation, the MEKF method has been applied to the design of micro attitude and heading reference system (AHRS) [19], as well as the MAV velocity and attitude estimation problem [20]. Although EKFs have been successfully applied in a number of applications, they have several disadvantages in addressing the navigation problem of MAVs.…”

Section: Related Workmentioning

confidence: 99%

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

Tang³

et al. 2015

Micromachines

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This paper presents a non-linear state observer-based integrated navigation scheme for estimating the attitude, position and velocity of micro aerial vehicles (MAV) operating in GPS-denied indoor environments, using the measurements from low-cost MEMS (micro electro-mechanical systems) inertial sensors and an RGB-D camera. A robust RGB-D visual odometry (VO) approach was developed to estimate the MAV's relative motion by extracting and matching features captured by the RGB-D camera from the environment. The state observer of the RGB-D visual-aided inertial navigation was then designed based on the invariant observer theory for systems possessing symmetries. The motion estimates from the RGB-D VO were fused with inertial and magnetic measurements from the onboard MEMS sensors via the state observer, providing the MAV with accurate estimates of its full six degree-of-freedom states. Implementations on a quadrotor MAV and indoor flight test results demonstrate that the resulting state observer is effective in estimating the MAV's states without relying on external navigation aids such as GPS. The properties of computational efficiency and simplicity in gain tuning make the proposed invariant observer-based navigation scheme appealing for actual MAV applications in indoor environments. OPEN ACCESSMicromachines 2015, 6 488

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Design and implementation of an Attitude and Heading Reference System (AHRS)

Cited by 19 publications

References 13 publications

Accuracy Improvement of Attitude Determination Systems Using EKF-Based Error Prediction Filter and PI Controller

Accuracy Improvement of Attitude Determination Systems Using EKF-Based Error Prediction Filter and PI Controller

Design of Unmanned Surface Vessels Experiment Platform

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

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