Aided Kalman Filter Models for GNSS-Based Space Navigation

Abstract

Ground-based assets traditionally aid space vehicle navigation, but the need for autonomy is steadily growing to meet the demands of future deep-space exploration. This paper proposes a customized Trajectory-Aware Extended Kalman Filter (TA-EKF) architecture, which conforms to the kinematic approach for Orbit Determination (OD) based on Global Navigation Satellite System (GNSS). Challenges at high altitudes, such as reduced GNSS signal availability and poor geometry, necessitate advanced filtering architectures leveraging external aiding data. When the receiver is not expected to interface with on-board guidance and control subsystems, aiding observations—in the form of a pre-mission planned spacecraft trajectory—allow to pursue precise and accurate OD only relying on GNSS measurements. Two alternative OAEKF designs are formulated, which foresee observation-domain and state-domain integration of aiding observations, respectively. While the former design acts directly on the filter posterior, the latter aims to overcome deficiencies in the state prediction owing to misspecified process dynamics. The feasibility of using terrestrial GNSS signals in Earth-Moon transfer orbits (MTOs) is thus demonstrated against aiding observation errors and mismodeling. The developed OAEKF models are thoroughly assessed via extensive Monte Carlo (MC) analyses, comparing their OD performance against a standalone EKF solution in a dedicated constellation simulator and mission planner.