Improved sensor fusion for flying laptop based on a multiplicative EKF

Abstract

Flying Laptop is a small satellite carrying an optical communications payload. It was launched in 2017. To improve the satellite’s attitude determination, which is used to point the payload, a new sensor fusion algorithm based on a low pass filter and a multiplicative extended Kalman filter (MEKF) was developed. As an operational satellite, improvements are only possible via software updates. The algorithm estimates the satellite's attitude from star tracker and fibre-optical gyroscope (FOG) measurements. It also estimates the gyroscope bias. The global attitude estimate uses a quaternion representation, while the Kalman filter uses Gibbs Parameters to calculate small attitude errors. Past Kalman filter predictions are saved for several time steps so that a delayed star tracker measurement can be used to update the prediction at the time of measurement. The estimate at the current time is then calculated by predicting the system attitude based on the updated past estimate. The prediction step relies on the low-pass-filtered gyroscope measurements corrected by the bias estimate. The new algorithm was developed as part of a master’s thesis at the University of Stuttgart, where Flying Laptop was developed and built. It was simulated in a MATLAB/Simulink environment using the European Space Agency’s GAFE framework. In addition, the new filter was applied to measurement data from the satellite. The results were used to compare the performance with the current filter implementation. The new Kalman filter can deal with delayed, missing, or irregular star tracker measurements. It features a lower computational complexity than the previous standard extended Kalman filter used on Flying Laptop. The mean error of the attitude estimate was reduced by up to 90%. The low pass filter improves the rotation rate estimate between star tracker measurements, especially for biased and noisy gyroscopes. However, this comes at the cost of potentially less accurate attitude estimates. Educational satellites benefit from the new algorithm given their typically limited processing power and cheap commercial-off-the-shelf (COTS) sensors. This paper presents the approach in detail and shows its benefits