Attitude determination represents a fundamental task for spacecraft. Achieving this task on small satellites, and nanosatellites in particular, is further challenging, because the limited power and computational resources available on-board, together with the low development budget, set strict constraints on the selection of the sensors and the complexity of the algorithms. Attitude determination is obtained here from the only measurements of a three-axis magnetometer and a model of the Geomagnetic field, stored on the on-board computer. First, the angular rates are estimated and processed using a second-order low-pass Butterworth filter, then they are used as an input, along with Geomagnetic field data, to estimate the attitude matrix using an unsymmetrical TRIAD. The computational efficiency is enhanced by arranging complex matrix operations into a form of the Faddeev algorithm, which is implemented using systolic array architecture on the FPGA core of a CubeSat on-board computer. The performance and the robustness of the algorithm are evaluated by means of numerical analyses in MATLAB Simulink, showing pointing and angular rate accuracy below 10° and 0.2°/s. The algorithm implemented on FPGA is verified by Hardware-in-the-loop simulation, confirming the results from numerical analyses and efficiency.

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A detumbling algorithm is developed to yield three-axis magnetic stabilization of a CubeSat deployed with unknown RAAN, orbit phase angle, inclination, attitude, and angular rate. Data from a three-axis magnetometer are the only input to determine both the control torque and the angular rate of the spacecraft. The algorithm is designed to produce a magnetic dipole moment which is constantly orthogonal to the geomagnetic field vector, independently of both the attitude and the angular rate of the rigid spacecraft. The angular rates are calculated in real time from magnetometer data, and the use of a second-order low-pass filter allows to rapidly reduce the measurement error within ±0.2 deg/sec. Numerical validation of the algorithm is performed, and a variety of feasible scenarios is simulated assuming the CubeSat to operate in low Earth orbit. The robustness of the algorithm, with respect to unknown deployment conditions, different sampling rates, and uncertainties on the moments of inertia of the CubeSat, is verified.