Q-Adaptation of SVD-aided UKF Algorithm for Nanosatellite Attitude Estimation

Abstract

Abstract In this study, we propose process noise covariance matrix adaptation (Q-adaptation) for the Singular Value Decomposition (SVD) aided Unscented Kalman Filter (UKF) algorithm. The main aim is to make the algorithm adaptive against the changes in the process noise covariance. The SVD-aided Adaptive UKF (SaAUKF) estimates the attitude and attitude rate of a nanosatellite. We implement the SVD method in the algorithm’s first phase using magnetometer and sun sensor measurements. It estimates the attitude of the nanosatellite giving one estimate at a single-frame. Then these estimated attitude terms are fed into the Adaptive UKF. The SaAUKF algorithm estimates the spacecraft attitude rates and provides finer attitude estimations. We investigate the performance of the algorithm when the process noise increases, which is very likely as a result of changes in the spacecraft dynamics in different environments. The results are compared with those of a non-Q-adaptive version of the algorithm.