Evaluation of a multiple model adaptive estimation scheme for space vehicle's enhanced navigation solution

This paper is mainly motivated by the outcome of a previous study carried out by the same authors on the subject of attitude estimation via a multiple model adaptive estimation (MMAE) scheme and serves as the follow-up study to further evaluate the potential of the MMAE scheme subject to higher fidelity models of both sensors and operating environments. The investigation carried out in this paper is aimed at answering the following design questions: (1) Will navigation solution mixing via MMAE truly offer an enhanced solution? (2) Will the MMAE architecture be more suitable for the multi-sensors (i.e., two star trackers, three-axis gyros, image-based sensors, etc) data mixing versus a single Extended Kalman Filter (EKF) design? (3) Will noise extraction and identification via MMAE offer a path to employ low-cost low-grade Micro-Electro-Mechanical Systems (MEMS) sensors? (4) Is it worth our while to consider the MMAE scheme as possible solution for future space vehicle subject to performance enhancement with lower grade and lower cost navigation sensors? The multiple sensors mixing via real-time adaptive mixing coefficients and autonomous switching among these on-board sensors at various operating conditions of a space vehicle's typical mission profile are also used as part of the design objectives in order to realistically evaluate the MMAE solution.