GNSS拒止下神经网络辅助的航天器集群协同导航OA

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 de-nial.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 dynami-cally 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 solv-ing method based on the rotation matrix is designed.A composite fault diagnosis mechanism based on the inertial inte-gral 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 de-signed to fit the potential relationship between GNSS and INS navigation data at historical moments and provide an on-line 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.