Improving Accuracy of Orbit Determination by Ground-Based Optical Observations Based on Artificial Neural Network

One of the most important steps to utilize space opportunities and dealing with space threats is to determine the position and estimate the condition of the objects in it. Due to the existence of orbital disturbances at different altitudes that make it impossible to accurately predict the trajectory of space objects for a long time, achieving stable methods to determine the exact orbit of these objects with the aim of managing space traffic and also optimally using space resources is an unavoidable necessity. Considering increasingly use of optical tools for space monitoring due to low price and easy access, besides inherent limitations in the number of observations and the accuracy of orbit estimation with classical methods, in this research we will investigate the ability of artificial neural network (ANN) to improve the accuracy of orbit determination of Space objects based on ground-based optical observations. Because of ANN’s universal approximation capability and flexible network structures, it has been found that the trained ANNs can achieve good performance in various situations, In the intended application of this research, the artificial neural network succeeded in significantly increasing the accuracy of satellite orbit determining at the epoch of observation, as well as reducing the orbit propagation error by several orders in the future epochs, which has important scientific and operational applications, especially for use in the field of Space Traffic Management and Space Situational Awareness.