Inertial space-based orbit estimation: A new measurement model for multiple observers

• Published in: Acta astronautica 2021-04, Vol.181, p.717-732
• Main Authors: Hippelheuser, James, Elgohary, Tarek A.
• Format: Article
• Language: English
• Subjects: Angles only measurements; Extended Kalman filter; Kalman filters; Line of sight; Nodes; Noise measurement; Orbit estimation; Planes; Satellite constellations; Satellite observation; Satellites; Space situational awareness
• ISSN: 0094-5765; 1879-2030
• DOI: 10.1016/j.actaastro.2020.05.033

Presented within this work is a new method for inertial orbit estimation of an object, either known or unknown, adaptable to a network of low-cost observation satellites. The observation satellites would only require a monocular camera for line of sight measurements. Using the line of sight measurements of each observer, a pair of orthogonal geometric planes that intersect both the observation satellite and the target are created. The intersection of the two planes in the inertial frame defines the new measurement model that is implemented with multiple observation nodes. Total system observability is analyzed and the instantaneous observability (per node) is used to remove “bad” measurements from the system. The measurement model is used in an extended Kalman filter framework and the measurement noise nonlinear transformation is addressed. Two cases are presented: first, a small number of observation nodes is analyzed to highlight the use of the instantaneous observability and its deleterious effect on the filter performance. Then, the method is expanded out to multiple observation satellites in a constellation. For both cases, the results show that this method is capable of producing accurate orbit estimation that converges in a short time. •Accurate, fast converging, inertial orbit estimation.•Based only on line of sight angle measurements.•Pair of orthogonal geometric planes intersecting both the observer and the target.•Application to a constellation of observation nodes.•Measurement covariance undergoes a nonlinear transformation.