The Effect of Simulink Block Kalman Filters in a CubeSat ADCS

Abstract: The purpose of this paper was to implement Kalman filtering in the form of pre-existing Simulink blocks into a CubeSat attitude determination and control system simulation and to evaluate their performance. In recent versions of Simulink, the block library has been expanded, providing a new level of abstraction for simulation engineers. The capabilities of such library filter blocks have previously not been explored for space applications and could offer a faster and more simplified filter integration process. Three types of filter implementations have been realized, being classic Kalman filter, extended Kalman filter and unscented Kalman filter. These have been applied to the outputs of the coarse Sun sensor and Earth horizon sensor, as well as to the simulation attitude estimate. State propagation functions have been defined in the form of constant and linear approximations in addition to state propagation following the same structure as the simulation reference truth. Filter efficiency was evaluated using control error, pointing knowledge, pointing accuracy and variance as performance measures. Furthermore, interventions were introduced in the form of sensor data loss and solar panel deployment. The Kalman filter blocks were successfully integrated into the simulation. Performance measures revealed that constant state transition functions provided the best performance in most cases, exceptions being the extended Kalman filter and unscented Kalman filter of the attitude estimate application. Here, the true state propagation instead outperformed the other filters. Signal data loss showed that the true state propagation was the only one that could accurately predict the attitude state in a scenario when sensors fail to provide data. Solar panel deployment could not be utilized to evaluate the filter performance as the filter implementation did not support prediction of a dynamic attitude state. Results suggest that the pre-existing Simulink filter blocks can provide an easier alternative to defining filters from scratch. However, great care needs to be taken when tuning block parameters and constructing state transition functions to assure proper behavior.