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.

Citation

Hua, S., Huang, H., Yin, F. and Wei, C. (2018), "Constant-gain EKF algorithm for satellite attitude determination systems", Aircraft Engineering and Aerospace Technology, Vol. 90 No. 8, pp. 1259-1271. https://doi.org/10.1108/AEAT-03-2017-0088