Sensor Data Fusion using Kalman Filter for LAPAN-A2 Satellite Attitude Estimation

Abstract

LAPAN A2 is a Low Earth Orbit (LEO) satellite with the primary mission to monitor the area. In order to fulfil the mission’s objectives, the Satellite attitude must be controlled properly. One of the control processes under consideration is the output attitude estimation. Attitude estimation processes in satellites mostly use satellite sensor output transformation. The estimation process, however, cannot be carried out with only one sensor where the generated transformation does not represent the satellite actual attitude. Thus, a fusion of two or more attitude sensors is needed. However, measurement noise, such as the effect of the Earth's albedo on the sun sensor and the interference of the Earth's magnetic field on the magnetometer, is incorporated into the estimation process. Thus, a filtering process is needed. Hence, this study aims to investigate the utilization of the sensor output raw data fusion combined with the Kalman Filter algorithm to estimate the LAPAN-A2 satellite attitude in the form of Euler angle.