ROBUST ESTIMATION FOR SATELLITE SWARMS IN PRESENCE OF OUTLIERS
Conference Year
2023
Abstract
The article presents a fresh approach to estimate the position and velocity of satellite swarms in Low Earth Orbit (LEO) while dealing with statistical outliers in sensor measurements. The method builds on the Robust Generalized Maximum Likelihood Kalman Filter (RGMKF) and relies on minimal sensor setup and inter-satellite communication. The framework assumes that all formation satellites have access to GPS through high-altitude navigation satellites and are equipped with sensors to estimate the relative position of neighboring satellites. By leveraging these sensors, the framework can maintain reasonable accuracy when GPS signals are absent or unreliable and improve estimation accuracy when GPS signals are reliable.
Primary Faculty Mentor Name
Hamid R. Ossareh
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Electrical Engineering
Primary Research Category
Engineering and Math Science
ROBUST ESTIMATION FOR SATELLITE SWARMS IN PRESENCE OF OUTLIERS
The article presents a fresh approach to estimate the position and velocity of satellite swarms in Low Earth Orbit (LEO) while dealing with statistical outliers in sensor measurements. The method builds on the Robust Generalized Maximum Likelihood Kalman Filter (RGMKF) and relies on minimal sensor setup and inter-satellite communication. The framework assumes that all formation satellites have access to GPS through high-altitude navigation satellites and are equipped with sensors to estimate the relative position of neighboring satellites. By leveraging these sensors, the framework can maintain reasonable accuracy when GPS signals are absent or unreliable and improve estimation accuracy when GPS signals are reliable.