PROJECT TITLE :
Autonomous navigation for lunar satellite using X-ray pulsars with measurement faults
X-ray pulsar-based mostly navigation (XNAV) is a navigation technique using celestial X-ray supply observations for spacecraft orbit determination. But, if the measurements are not reliable because of any quite malfunction, the performance of XNAV could lead to considerable errors and even divergence. In this study, a brand new algorithm called robust extended Kalman filter (REKF) is proposed for the lunar satellite autonomous navigation system, that is robust against measurement malfunctions. Initial, the satellite dynamic model applied perturbations springs underneath the J2 perturbation of the Moon. The pulse time-of-arrival (TOA) is used to make the observation model. Second, the performance of XNAV system is discussed by analysing the transformation error of TOA, system observability and controllability. Then, an adaptive measurement noise scale factor (AMNSF) is designed by using the innovation sequence in REKF. Meanwhile, the gain matrix is modified by adding the AMNSF to cut back the influence of malfunction and enhance the robustness of XNAV system. Finally, the simulation results show the proposed navigation scheme is valid and possible, and it's appropriate for lunar satellite autonomous navigation.
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