Multiple event triggers in linear covariance analysis for orbital rendezvous
Sievers, Adam M.
Spanos, Pol D.
Master of Science
Linear covariance analysis is a powerful tool for spacecraft rendezvous analysis and design. This methodology is capable of generating results which compare well with the results of a Monte Carlo simulation while requiring dramatically less computation time. The introduction of multiple events triggered on state conditions causes discrepancies between the linear covariance analysis and theoretical results. This thesis introduces techniques for applying multiple event triggers to linear covariance analysis. The proposed technique is validated by comparison with a Monte Carlo simulation involving 1000 runs. The trajectories generated by the Monte Carlo simulation are compared to the 3Q trajectory dispersions from linear covariance analysis. Further, the Monte Carlo simulation navigation filter and the linear covariance analysis on-board covariance matrices are examined in detail. A time of arrival dispersion analysis is performed for each event trigger and an event near the end of the trajectory.