scholarly journals Invariant Extended Kalman Filter: theory and application to a velocity-aided attitude estimation problem

2009 ◽  
Author(s):  
Silvere Bonnabel ◽  
Philippe Martin ◽  
Erwan Salaun
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Estimation Problem ◽  
Filter Theory

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

scholarly journals Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

2016 ◽  
Vol 2016 ◽  
pp. 1-24 ◽  
Author(s):  
Romy Budhi Widodo ◽  
Chikamune Wada
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Compensation Model ◽  
Measurement Noise Covariance ◽  
Noise Covariance ◽  
The One ◽  
The Right ◽  
External Acceleration

Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

scholarly journals Three-Axis Attitude Estimation Of Satellite Through Only Two-Axis Magnetometer Observations Using LKF Algorithm

2015 ◽  
Vol 22 (4) ◽  
pp. 577-590 ◽  
Author(s):  
Mohamad Fakhari Mehrjardi ◽  
Hilmi Sanusi ◽  
Mohd. Alauddin Mohd. Ali
Keyword(s):  
Kalman Filter ◽  
Space Missions ◽  
Attitude Estimation ◽  
Estimation Problem ◽  
Sensor Data ◽  
Computationally Efficient ◽  
Satellite Attitude ◽  
Simulation Results

Abstract Estimation of satellite three-axis attitude using only one sensor data presents an interesting estimation problem. A flexible and mathematically effective filter for solving the satellite three-axis attitude estimation problem using two-axis magnetometer would be a challenging option for space missions which are suffering from other attitude sensors failure. Mostly, magnetometers are employed with other attitude sensors to resolve attitude estimation. However, by designing a computationally efficient discrete Kalman filter, full attitude estimation can profit by only two-axis magnetometer observations. The method suggested solves the problem of satellite attitude estimation using linear Kalman filter (LKF). Firstly, all models are generated and then the designed scenario is developed and evaluated with simulation results. The filter can achieve 10e-3 degree attitude accuracy or better on all three axes.

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