Multiple super-agile satellite collaborative mission planning for area target imaging

Collaboration between multiple super-agile satellites with dynamic imaging capabilities provides an opportunity for highly efficient observation of large-area targets. Reasonable allocation of multi-satellite resources and determination of the action sequences of each satellite are two key requirements for multiple super-agile satellite mission planning. We propose a multi-satellite collaborative mission planning approach for an area target imaging mission, considering the dynamic imaging characteristics, achieving integrated optimization of the allocation of multi-satellite resources and determination of the action sequences of each satellite. A multi-satellite mission planning model with two objectives was established, with the objective functions being to maximize the observation coverage revenue and minimize the task performance time. An improved particle swarm optimization algorithm, represented by introducing particle learning/selection strategies and variable type-adaptive position updating methods, addresses the discrete–continuous hybrid variables in this optimization model. The proposed method was verified by constructing three groups of comparative experiments with area targets of different sizes. The proposed method showed consistent adaptability to various complexities and achieved synchronous optimization of the allocation of multi-satellite resources and determination of the action sequence of each satellite in areas of different sizes. Compared with existing algorithms, the proposed algorithm obtained superior imaging mission schemes with lower computational consumption, higher observation coverage revenues, or shorter task performance time. The experimental results showed that the proposed multi-satellite collaborative mission planning approach could be used to the ground operational systems of super-agile satellite constellations.