ATTITUDE DETERMINATION STUDIES FOR EOS-AM1

摘要

Previous applications of the theory of Kalman Filter-based attitude determination, with simultaneous gyro bias estimation, to the Earth Observation System (EOS) AM1 mission have reported single-axis and multi-axis performance predictions and sensitivity of the attitude determination performance to significant system parameters. The present study extends these results by quantifying the predictive capability of covariance analysis as compared with Monte Carlo analysis, showing that thrice square root of the Kalman filter covariance matrix diagonal is a reasonable prediction for the 99.7% Monte Carlo results, but not of worst-case performance. The present study establishes further insight into sensitivity of EOS attitude determination performance to simulated stellar-lunar geometries by comparing Monte Carlo performance predictions using a statistically-generated star field (including statistical lunar blockage gaps) with those using a physical model (real star field, lunar and solar ephemerides). This study further demonstrates that, with EOS parameters, the performance is driven by the sensor noise rather than the gaps in the star field, and confirms these conclusions by Monte Carlo assessments of the orbits with short and long star gaps.