Augmented unbiased minimum-variance input and state estimation for tracking a maneuvering satellite. (October 2019)

• Record Type: Journal Article
• Authors: Jiang, Yuzi; Baoyin, Hexi; Ma, Pengbin
• Is Part Of: Acta astronautica. Volume 163(2019)Part B
• Page Start: 96
• Page End: 107
• Publication Date: 2019-10
• Subjects: Orbit determination -- Input estimation -- Maneuver detection -- Maneuver tracking -- Extended Kalman filters; Astronautics -- Periodicals; Outer space -- Exploration -- Periodicals; Astronautics; Periodicals; 629.405
• DOI: 10.1016/j.actaastro.2018.11.015
• Languages: English
• ISSNs: 0094-5765

Abstract

Abstract: The technique of tracking a maneuvering satellite is significantly important for Space Situation Awareness (SSA). Traditional Kalman filters (KF) cannot robustly track unknown maneuvers. Motivated by the problem of tracking a non-cooperative maneuvering satellite, an augmented unbiased minimum-variance input and state estimation (AUMVISE) method is developed for estimating the state and the maneuver acceleration in this study. The maneuver acceleration estimate of the proposed method is proven to be more accurate than the original unbiased minimum-variance input and state estimation (UMVISE) method. Approaches based on the measurement residuals and the acceleration estimates are developed for maneuver start and end detection. The filter is switched between the AUMVISE method and the classical extended Kalman filter (EKF) to obtain an accurate tracking result during both the maneuver period and the non-maneuver period. Simulation results show that the proposed method has a suitable maneuver detection delay and outperforms the UMVISE method in estimating the state and maneuver acceleration. Highlights: An input and state estimation method is proposed for maneuver tracking. Maneuvers are detected by innovations and acceleration estimations. High-precision state and acceleration estimation under unknown maneuvers. Better tracking performance compared with UMVISE method.