Abstract
The number of satellites and debris in space is dangerously increasing through the years. For that reason, it is mandatory to design techniques to approach the position of a given object at a given time. In this paper, we present a system to do so based on a database of satellite positions according to their coordinates (x,y,z) for one month. We have paid special emphasis on the preliminary stage of data arrangement, since if we do not have consistent data, the results we will obtain will be useless, so the first stage of this work is a full study of the information gathered locating the missing gaps of data and covering them with a prediction. With that information, we are able to calculate an orbit error which will estimate the position of a satellite in time, even when the information is not accurate, by means of prediction of the satellite’s position. The comparison of two satellites over 26 days will serve to highlight the importance of the accuracy in the data, provoking in some cases an estimated error of 4% if the data are not well measured.
Highlights
The number of satellites and artificial objects orbiting in space around the earth has grown exponentially in recent decades due to the reduction of production costs and technological advances that facilitate their launch
2019, with a much higher forecast for the coming years [1], as seen in Table 1 published by the European Space Agency (ESA) about the debris figures [2]
The debris problem has been a matter of study and concern since the United Nations published a report in 1999 warning about this forthcoming problem [3], focusing its information on orbital debris measurements, modeling, risk assessments, and mitigation measures
Summary
The number of satellites and artificial objects orbiting in space around the earth has grown exponentially in recent decades due to the reduction of production costs and technological advances that facilitate their launch. Other machine learning techniques have been used to improve orbit prediction accuracy as in [15] implementing LSTM networks to provide atmospheric density predictions, in [16] convolutional neural networks to work with special images to detect space objects, or in [17,18,19] where neural networks were used, for example, to explore the solar gravity-driven orbital transfers in the Martian system. These studies started with a uniform, process-ready dataset. This way we will be able to provide more accuracy when calculating our orbit’s parameters, and so the highest accuracy on our error calculation and prediction model
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