Dp 521 en
Sensor Fusion Solutions for Upcoming CubeSatMissions
Author: Mariana Barbosa
In this thesis, an extended Kalman filter formulation for attitude estimation is developed for implementation in a small satellite simulator using inertial sensors. Two different attitude parametrizations are analyzed: quaternions and modified Rodrigues parameters. Simulations and experiments are conducted to validate each Kalman filter algorithm. This thesis also investigates the deterministic and random error sources of sensors measurements. A calibration procedure is proposed and conducted to eliminate the deterministic error sources. The random error is modeled with the Allan variance formulation and it is integrated in the Kalman filter. The results for the simulations with both attitude parametrizations are found to yield accurate attitude solutions. The advantage of the quaternion approach is that it does not have singularities, while the advantage of the modified Rodrigues parameters approach is that it is simpler since it has one less state. Experimental tests were conducted on a one-degree of freedom air bearing. The results from experimentation indicate that in reality, the filter results in a less accurate attitude solution than predicted by simulation. Suggestions to improve the filter performance are proposed.