A DNN-based method for calculating minimal inter-satellite distances
Aiming at the shortcomings of the minimal inter-satellite distance solving methods,such as the general low efficiency and weak applicability,a method that can balance the applicability,accuracy and computational efficiency when solving the minimal inter-satellite distance for non-periodic coplanar relative motion of a pair of satellites is proposed.The principal idea of this method is utilizing the advantage of Deep Neural Networks(DNN)to fit the relationship between the initial relative state of two satellites and the minimum inter-satellite distance.The proposed method is able to calculate the minimal distance only through the position and velocity information at any given initial time.The close-range relative motion equations between satellites are established,based on which the analytic inter-satellite distance function is obtained.Through the analysis and derivation of the function,the range of the minimal inter-satellite distance with time is analytically obtained.The construction method of the dataset,which maps the initial states and the minimal distances,is designed to obtain a great number of mappings.Then a model to train the DNN is established and the training process is conducted repeatedly until the convergence is achieved.The training results of the DNN are verified through different simulation examples.The results demonstrate that the DNN-based calculation method can compute the minimal distance nearly in real-time and the corresponding relative errors are almost below 0.2％.
万方数据
维普
