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Speed and angle measurement sensors-based federal robust adaptive smooth variable structure Kalman filter for Mars probe autonomous navigation
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DOI：10.1007/s11431-025-3212-1.png)
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Abstract
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This paper presents a federal robust adaptive smooth variable structure Kalman filter (RASVSKF) algorithm based on speed and angle measurement sensors for spacecraft autonomous navigation during the cruise phase to Mars. Firstly, the orbital dynamic model of Mars probe during cruise phase, and also the angle and speed measurement models are constructed. The integration of the speed and angle measurement sensors largely reduces the dependence on complex orbital dynamic model and also ground-based radio support, and also provides high-precision autonomous navigation. Moreover, by integrating a robust adaptive Kalman filter with a smooth variable structure filter, the algorithm enhances estimation accuracy and robustness against abrupt maneuvers. The federal architecture allows for efficient data fusion from multiple sensors, improving overall system reliability. Numerical simulations demonstrate that the federal RASVSKF algorithm achieves higher accuracy in position and velocity estimation and greater robustness compared with the traditional federal Extended Kalman Filter algorithm, making it more suitable for deep-space navigation applications.
Keywords:
deep-space navigation
federal filter
robust adaptive Kalman filter
variable structure filter
Mars exploration
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