METHOD FOR SHORT-TERM PREDICTION OF THE ACCURACY CHARACTERISTICS OF THE GLONASS SYSTEM USING PRECEDENTS
The article is devoted to the development of a method for short-term forecasting of the accuracy characteristics of the GLONASS system, which is based on the training of a recurrent neural network on a set of measuring information received from the "Glonass-M" series spacecraft. The relevance of predicting the accuracy characteristics of the GLONASS system is due to the increasing level of complexity of the tasks solved by the GLONASS system control center, as well as the lack of implemented methods for predicting the state of the orbital constellation of GLONASS spacecraft. The proposed method includes an algorithm for recovering the lost measurement information and a private algorithm for back propagation of an error when training a recurrent neural network. The application of this method made it possible to predict the value of the accuracy characteristics of the GLONASS system with an accuracy that allows timely taking measures to plan the use of the orbital constellation for its intended purpose. A qualitative assessment was made by comparing the real and predicted values of the accuracy characteristics of the GLONASS system.