Constant-gain EKF algorithm for satellite attitude determination systems

Abstract

Purpose This paper aims to propose a constant-gain Kalman Filter algorithm based on the projection method and constant dimension projection, which ensures that the dimension of the observation matrix obtained is maintained when there is a satellite with multiple sensors. Design/methodology/approach First, a time-invariant observation matrix is determined with the projection method, which does not require the Jacobi matrix to be calculated. Second, the constant-gain matrix replaces the EKF (extended Kalman filter) gain matrix, which requires online computation, considerably improving the stability and real-time properties of the algorithm. Findings The simulation results indicate that compared to the EKF algorithm, the constant-gain Kalman filter algorithm has a considerably lower computational burden and improved real-time properties and stability without a significant loss of accuracy. The algorithm based on the constant dimension projection has better real-time properties, simpler computations and greater fault tolerance than the conventional EKF algorithm when handling an attitude determination system with three or more star trackers. Originality/value In satellite attitude determination systems, the constant-gain Kalman Filter algorithm based on the projection method reduces the large computational burden and improve the real-time properties of the EKF algorithm.