Surrogate model-based calibration of a flying Earth observation satellite.

At the European Space Agency (ESA), Modeling and Simulation (M&S) plays a fundamental role during the lifetime of a spacecraft, being used from the design phase to the testing and during operations in space. M&S tools embed general physics-based models and disciplines characterized by configurable parameters which have to be calibrated in order to mimic the behavior of the actual flying spacecraft. However, their calibration requires a large number of simulations which are unfeasible to be obtained through computationally expensive high-fidelity simulation models. Thus, the inability to calibrate the high-fidelity simulation models poses limitations for the use of M&S tools during spacecraft operations. In this light, the present work proposes the use of a surrogate model-based approach for the calibration of simulation models of spacecraft. The approach integrates a computationally inexpensive deep-learning-based surrogate model, which mimics the high-fidelity simulation model without requiring the same computational burden, and a metaheuristic optimization algorithm, to identify the optimal values of the simulation model configurable parameters. This enhances the capabilities of M&S tools and allows their use in operations. The approach's effectiveness is shown by its application to real flying Earth observation satellite data and simulation models. [ABSTRACT FROM AUTHOR]