Estimation of Spacecraft Thermal Mathematical Models Based on Data Assimilation

A study on a self-tuning technique for system noise settings

Abstract

Spacecraft temperature changes on orbit can be predicted based on thermal mathematical models, whose accuracies play important roles for efficient thermal designs or effective operations. The authors have studied on the application of data assimilation techniques to parameter estimations or temperature predictions of thermal mathematical models. From the study, it was found that settings of system noise, which can be tuned arbitrary, have strong effects on both the efficiency and the accuracy of estimation results. In our research, we apply a innovation based self-tuning algorithm for system noise, which have been sometimes used in the conventional Kalman filter, to the spacecraft mathematical estimation problem and investigate the effectiveness of the tuning technique.