Static and Dynamic Attitude Decomposition for Estimation with Magnetometer Sensor
| Authors: | Changey Sebastien, SUPELEC, France
Beauvois Dominique, SUPELEC, France
Fleck Volker, ISL - French-German research institute of Saint-Louis, France |
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| Topic: | 7.5 Intelligent Autonomous Vehicles |
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| Session: | Intelligent Autonomous Vehicles |
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| Keywords: | Kalman filters, Attitude, Magnetic fields, Models, Estimation algorithms, Nonlinear filters |
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Abstract
A priori information given by the complete modelling of the ballistic behavior of a projectile is simplified to give a pertinent reduced evolution model. This model is composed of quasi-static and dynamic models. An extended Kalman filter is designed to estimate dynamic part of the 3 attitude angles (roll in [0; 2 pi], angle of attack and side-slip in the range of few milliradians) from measures of the magnetic field of the earth given by a three-axis magnetometer sensor embedded on the projectile.The algorithm has been tested in simulation, using realistic evolution of attitude data with measurement noise.
