SVD-Aided UKF Adaptation for Nanosatellite Attitude Estimation under Uncertain Process Noise Conditions

Abstract

In this work, the adaptation of the process noise covariance matrix for the nontraditional attitude filtering technique is discussed. The nontraditional attitude filtering technique integrates the unscented Kalman filter (UKF) and singular value decomposition (SVD) approaches to estimate the attitude of a nanosatellite. It is shown in this study that the process noise bias and process noise increment type system changes will cause a change in the statistical characteristics of the innovation sequence of UKF. The influence of these types of changes on the innovation of UKF is investigated. For differences between the process channels, the Q (process noise covariance) adaptation strategy with multiple scale factors is specifically recommended. We analyze the performance of the multiple scale factors-based adaptive SVD-aided UKF (ASaUKF) in the cases of process noise increment and bias that can be caused by variations in the satellite dynamics or space environment. The adaptive and nonadaptive variants of the nontraditional attitude filter are compared through simulations in order to estimate the attitude of a nanosatellite.