Estimation of the Relative States of Satellite Formation Flights Using the Adaptive Extended Kalman Filter

Abstract: Using a satellite cluster design to reduce mission costs, mission request complexity, and single satellite utilization limits has grown in popularity in recent years. This paper presents two-satellite formation designs based on the pseudo-ranging model (GPS-based distance model). The Newton−Raphson Method (NRM) and the Global Positioning System (GPS) are used to create a novel approach to satellite relative navigation architecture. The Adaptive Extended Kalman filter (AEKF) with measurement noise covariance scaling is used to estimate the relative locations of the target and tracker satellites using the NRM technique. The relative location and velocity of the satellites are computed using the Hill–Clohessy–Wiltshire (HCW) equations. Within the scope of the advancement of studies with EKF in the literature, the focus of this research is to improve relative estimations with the adaptive filter by accounting for measurement or dynamic model problems.