Introduction Deep space exploration, as a critical means for humanity to understand and investigate the evolutionary history of the universe, has entered a phase of rapid development. A single deep space navigation mode struggles to meet the high-precision and high-reliability requirements for long-duration mission operations in highly dynamic and extreme environments. Hence, multi-mode resilient navigation will be the inevitable future approach for deep space exploration. Methods The study proposes a set of deep space resilient navigation models specifically for gravity assist phase, including X-ray pulsar navigation, Very Long Baseline Interferometry (VLBI) navigation, celestial body navigation, and combined navigation using these methods. Various navigation filtering algorithms applicable to deep space models are introduced, and the performance of the navigation models under different conditions is analyzed through simulation. For six gravity assist phases with the flight sequence proceeding as Earth, Venus, Venus, Earth, Jupiter, Neptune and heliosphere bottom, three navigation filtering methods, which are Extended Kalman Filter (EKF), Game theory-based H∞ filter, and Robust Student’s t Kalman Filter (RSTKF), exhibit consistent performance for X-ray pulsar navigation. Besides, ground-based VLBI observations can be utilized throughout the gravity assist phase and exhibit superior navigation performance compared to X-ray pulsar navigation. Results The performance of the celestial body navigation is significantly worse than that of X-ray pulsar and VLBI navigation, with the accuracy degrading as mission distance increases. The positioning errors of the resilient navigation approach are within 10 km across all tested scenarios. Moreover, the feedback-free resilient navigation demonstrates slightly better performance than the fusion feedback mode, though the latter maintains high-frequency output consistency with astronomical navigation subsystems. Discussion These results provide the significant theoretical support and practical reference for advancing deep space navigation.
Zhu et al. (Tue,) studied this question.