A roll attitude determination method based on the jamming energy of GEO satellites and an LSTM neural network

Roll attitude determination is crucial for rotating vehicle attitude determination. As research in these areas continues, numerous attitude determination methods have been introduced. Roll attitude determination methods have played a key role in acquiring vehicle information from the Global Navigation Satellite System (GNSS), which was rapidly developed. However, in existing methodological studies, satellite energy information has not been sufficiently analyzed and utilized. This paper presents a roll attitude determination method based on the jamming energy of geostationary orbit (GEO) satellites and a long short-term memory (LSTM) neural network. In this study, a comprehensive model of the energy and roll angle is presented, and the complex properties and common laws of the actual received GEO satellite energy is analyzed. After real-time roll attitude testing of a rotating vehicle using different methods, the proposed method is found to be superior to the traditional least squares (LS) method, with a 48.97% reduction in the mean self-error and a 48.20% reduction in the mean Hall standardized error for the determined roll angles. The research results show that the proposed LSTM deep learning method is more conducive for restoring the complex energy properties of GEO satellites and further enabling accurate real-time roll attitude determination.