Neural network-assisted cooperative navigation of spacecraft clusters under GNSS denials

To address the problems of navigation dispersion in the Global Navigation Satellite System (GNSS) short-term denial scenario during the on-orbit autonomous navigation of spacecraft clusters, we introduce a Data-Link-based (DL) relative ranging system and a neural network auxiliary module to improve the navigation effect under GNSS denial. Considering the different sampling frequencies of INS, GNSS, and DL systems, this paper constructs the Factor Graph (FG) architecture, which has the feature of plug-and-play, and adopts the belief propagation rules to dynamically fuse the multi-source measurement information of INS/GNSS/DL, enabling spacecraft cooperative positioning. To avoid the error amplification when solving the ranging information of the data links, a relative ranging information solving method based on the rotation matrix is designed. A composite fault diagnosis mechanism based on the inertial integral state and the historical measurement residuals is designed, which together with the plug-and-play characteristics of the FG, enables detection and isolation of the GNSS faults and DL faults. Moreover, for the GNSS denial case, an auxiliary system combining General Regression Neural Network (GRNN) and Elman Neural Network (ENN) is designed to fit the potential relationship between GNSS and INS navigation data at historical moments and provide an online prediction and compensation of missed GNSS navigation data. The predicted GNSS data are further fused with the INS/DL data to obtain the combined navigation results under the GNSS denial case. The simulation results show that the introduction of datalink-based relative ranging information and the GRNN-ENN neural network-assisted system can effectively mitigate the navigation divergence in the GNSS denial case.