LAUSR

Abstract The advance of space technology allowed small satellites to accomplish missions that were once only possible with big and expensive platforms. The quality and accuracy of small sensors have also improved, leading to a better knowledge of the spacecraft attitude. However, the integration and assembly process of such platforms has constraints that often hinder a high accuracy placement and calibration of the equipment. This translates into the three most common errors in sensor measurements: bias, misalignment, and non-orthogonality. This work proposes a new algorithm designed to estimate and correct those three error sources for any sensor based on vector observations. The algorithm is based on the same principle used by inertial navigation systems with non-inertial information. A propagator computes the attitude based on the gyro readings with the initial estimation provided by the other sensors. Concurrently, a Kalman filter estimates the attitude and sensor errors. After filter convergence, the estimation is used to correct the attitude knowledge. An observability analysis is carried out, showing in which conditions the filter can correctly estimate the error state. Afterward, the proposed technique is tested, employing a Monte Carlo simulation in a validated satellite simulator. The results show that the algorithm can significantly improve attitude estimation accuracy during different satellite operating modes. At last, the filter robustness is assessed by simulating the system with huge errors. This test shows that the filter can converge even in such a challenging scenario, providing excellent accuracy.