Speaker
Dr
Juan Félix San-Juan
(Scientific Computing Group (GRUCACI), University of La Rioja)
Description
Artificial Intelligence in general, and machine learning in particular, are the cornerstones of the so-called Industry 4.0. Being aware of the new opportunities provided by current technology and increased computational power, public administrations are fostering this upcoming fourth industrial revolution, in which both the private sector and academia are also taking an active part. The space sector, which is not an exception to this tendency, is looking for a similar advance to Industry 4.0. Among many other needs, improvement in the accuracy required for the solution of some practical Space Situational Awareness (SSA) problems is being actively sought. In particular, current needs in orbit determination and prediction, especially in the framework of SSA, are demanding innovative approaches. During the last years, we have been applying machine learning and statistical techniques to improve the accuracy of any kind of orbit propagator. For that purpose, we characterize the deviation of the propagator by means of the time series of its error with respect to observations or pseudo-observations, that is, accurately computed ephemerides. Then, we model that deviation through machine learning methods, such as neural networks or gradient boosting machines, and also by means of statistical methods. Finally, we are able to predict deviation values in the future, based on the developed model. In this talk, we will present this methodology, which we have called hybrid propagation, the basics of neural networks, the machine learning software that we use, the necessity to fine-tune hyper- parameters, the importance of developing parsimonious models, and the results of an illustrative example in which we correct the error of the well-known SGP4 propagator for Galileo orbits.
Primary author
Dr
Juan Félix San-Juan
(Scientific Computing Group (GRUCACI), University of La Rioja)
