Onboard satellite visibility prediction using metamodeling based framework

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Satellite autonomous systems are employed to address complex space applications through onboard data processing and mission planning. To take advantage of onboard autonomous systems, rapid onboard satellite visibility predictions are necessary for certain decision-making missions, including Earth observation resource allocation and satellite data transmission. We consider this visibility prediction process as a roots-finding problem for a multiple hump function, and design a metamodeling-based framework with a self-adaptive interpolation method. Metamodels are developed as surrogates for visibility prediction functions to reduce expensive computational costs. Our proposed framework has a broad range of applications for all orbital types and orbit propagators. We conduct the experiments using different metamodeling techniques, radial basis functions, Kriging, and support vector regression based upon real China's satellites. Numerical simulations indicate that the proposed framework outperforms existing interpolation methods, efficiently reducing the onboard computational cost.