Identification of a Tethered Satellite Using an Extended Kalman Filter

Abstract

Recent studies involving a tethered satellite system(s) (TSS) have increased due to the importance of accurately identifying and analyzing the motion of a TSS. If the motion of a tethered satellite is not accurately identified, the satellite could be mistaken as a ballistic threat. Standard orbit determination methods used today are unable to identify a tracked satellite as part of a TSS, due to the non-Keplerian nature of its motion. Accurate identification of a TSS becomes more complicated with the need to perform this process quickly using a small set of observational data. Once this “quick-look” identification process is performed, it is necessary to calculate the critical orbit determination parameters used for future TSS tracking and prediction. An extended Kalman filter (EKF) has been developed to perform both the state estimation and quick-look identification processes for a tethered satellite not known a priori as being part of a TSS. In the application of the EKF to a TSS, both, manual tuning and adaptive tuning methods were used. The adaptive tuning method used is based upon ridge-type filtering techniques involving the computation of a biasing parameter that is used as input into the process noise matrix, which is required in tuning the EKF. The overall performance of the EKF is presented for varying tether lengths, tether orientation, and observation noise levels. The results obtained from the adaptively-tuned EKF are presented in this thesis and are compared to those obtained from a batch filter and manually-tuned EKF presented in recent studies.