Modeling orbital propagation using regression technique and artificial neural network

Abstract

Orbital propagation models are used to predict the position and velocity of natural and artificial objects orbiting the Earth. It is crucial to get accurate predictions to ensure proper satellite operational planning and early detection of possible disasters. It became critical as the number of space objects grew due to many countries scrambling to explore space for various purposes such as communications, remote sensing, scientific mission, and many more. Physical-based and mathematical expression approaches provide orbital propagation with high accuracy. However, these approaches require substantial expenditure to provide suitable facilities and are complicated for those with no expertise in this field. The orbital propagation model is developed using regression techniques and artificial neural networks in this study. The aim is to have a reliable and precise orbital propagation model with minimal computational and cost savings. The past orbital data is used instead of complicated numerical equations and expensive tools. As a result, the trained orbital propagation model with accuracy up to 99.49% with a distance error of 18.73km per minute is achievable. The trained model can be improved further by modifying the network model and various input data. This model is also expected to provide vital information for organizations and anyone interested. Finally, this research can help organizations with insufficient resources to have their orbit propagation model without special tools or rely on other countries with satellite data at a lower cost.