Orbit Estimation Using Track Compression and Least Squares DifferentialCorrection

摘要

This thesis develops two methods of compressing a track of radar observations ofa satellite into a single state vector and associated covariance matrix, and a method of estimating orbits using results from multiple tracks. The track compression uses least squares differential correction to determine a state vector at the central observation time. The resulting state vectors and covariance matrices are then used to estimate the satellite's orbit, also using least squares differential correction. Numerical integration using two-body, J2 and an atmospheric drag model is used to represent the dynamics. This orbit estimation produces a state vector which includes the ballistic coefficient, as well as an associated covariance matrix. Finally, a one-fiftieth scale demonstration of the full AFSPC catalog of satellites and debris is conducted to demonstrate the improvement in accuracy over current practice which results. The truth model includes J2 zonal harmonic effects and an atmospheric drag model. This demonstration shows that the orbits of 90% of the entire catalog of objects can be estimated with sufficient accuracy to allow position determination within one kilometer after only two days of tracking. Within four days, most satellite positions are determined within fifty meters.