ESA GNC Conference Papers Repository
Linear Covariance Propagation for Atmospheric re-Entry
Monte Carlo simulation is a very good method to assess the precision of a system. Therefore, it is commonly used to verify GNC accuracy, although computation power and time needed for this method is very high. In order to obtain approximated GNC accuracy results much quicker, linear covariance propagation can be used. This method is based on the propagation of the covariance of dispersions around a reference case. To propagate this covariance, the equations describing the systems evolution are linearized around the reference case and then integrated. In order to represent as well as possible the systems architecture, the usual equations of linear covariance propagation are modified. This way, the different kinetics of the guidance navigation and control subsystems are better respected, and the model gets consequently closer to the actual systems behaviour. To support this new formulation of the equations, the covariance propagation process is adapted. Linearization of the equations does not necessarily imply over-simplification of the model. Indeed, this study shows that a problem as complex as atmospheric re-entry can successfully be described by a linear covariance propagation tool. Closed loop algorithms are implemented, as for example navigation error model, guidance principle and actuator behaviour. Their influence on the accuracy of the re-entry module is evaluated. Focus is made on navigation error model that takes into account an inertial measurement unit as well as additional sensors, such as GNSS or star-trackers. To finish, interactions between navigation errors, command corrections and trajectory dispersions are assessed for different configurations.