Noise estimation for star tracker calibration and enhanced precision attitude determination

Abstract

This paper presents the design, development, and validation of a nonlinear least square estimation scheme applied to star tracker noise extraction and identification. The paper is the by-product of a Post-Launch Test (PLT) tool development effort conducted by two independent teams, Swales/NASA and Boeing. The main objective is to have a set of tools ready to provide on-orbit support to the GOES N-Q Program. GOES N-Q employs a stellar inertial attitude determination (SIAD) system that achieves high precision attitude estimation by processing attitude and rate data provided by multiple star trackers (ST) and an inertial reference unit (IRU), respectively. The key component of SIAD is the ST. The ST's star position vector is corrupted by three major noise sources: temporal noise (TN), high spatial frequency noise (HSF), and low spatial frequency (LSF) noise. The last two noise sources are not while and correlated. As a result, the performance of the SIAD filter is no longer optimal, causing the reconstructed attitude knowledge to potentially satisfy requirements with a narrow margin. This tight margin is critical and may affect the GOES N-Q mission, particularly the Image Navigation and Registration (INR) system performance. The PLT toolset is expected to provide the capability to mitigate this potential problem during PLT time.