Linear covariance analysis for autonomous space rendezvous missions

This thesis presents the development process of a mission analysis tool using Linear Covariance Analysis applied to space rendezvous missions. The work has been conducted in the company Thales Alenia Space. Linear Covariance Analysis is a powerful mathematical tool, which aims to provide an alternative and more agile solution to Monte Carlo methods for stochastic mission analysis. While it comes with a trade-off in accuracy, it allows for much faster development, as it can get the same statistical results obtained with Monte Carlo in a single run. The only overhead of this method is the need for linearization, which finds its applicability through CW equations to the space rendezvous problem. The theoretical background of Linear Covariance Analysis is presented, and its adaptation for the space rendezvous domain. Its applicability is evaluated, through validation by Monte Carlo analysis. Lastly, multiple applications of this method are analysed.