Modified State Observer for Atmospheric Reentry Uncertainty Estimation

Abstract

Novel techniques, known as the Modified State Observer (MSO) and Reduced Order Modified State Observer (RMSO), are implemented to estimate system states and uncertainty due to unmodeled or unknown phenomena in a system. The MSO and RMSO utilize neural networks to calculate the system uncertainty online. A useful application of these observers is the state and uncertainty estimation during atmospheric entry or reentry. Uncertainty is inherent during atmospheric reentry due to variations in atmospheric density and the attitude of the reentering object. The ability of the MSO and RMSO to estimate system uncertainty online helps to provide more accurate state estimates in systems with large uncertainty. To demonstrate the applicability and validity of the MSO and RMSO to these systems, a simulation is performed assuming no a priori knowledge of reentry dynamics to a nonlifting atmospheric reentry and the results of extended Kalman Filters are presented for comparison. The MSO and RMSO are then applied to a tumbling reentry scenario for comparison to the nonlifting reentry scenario. Results are presented that demonstrate the ability of the MSO and RMSO to accurately estimate both the system states and the system uncertainty during an atmospheric reentry.