A review of the frameworks, models, and algorithms for large-scale imaging satellite mission planning

Abstract

Imaging satellite mission planning plays a central and crucial role in the operation control of large-scale imaging satellite constellations. To such satellite constellations, the mission planning frameworks and models for single satellites and small-scale satellite constellations are not applicable any longer, while some main algorithms can still be used. In such a context, the frameworks, models, and algorithms for large-scale imaging satellite mission planning (LSISMP) are reviewed, and some possible future research directions are pointed out. Firstly, three types of planning frameworks (centralized, distributed, and centralized-distributed ones) are discussed, and their advantages and disadvantages as well as application scenarios are deeply analyzed. The importance and role of different mission planning frameworks in large-scale imaging satellites are revealed. Then, the decision forms and common features of LSISMP are unveiled from perspectives of mathematical programming models, combinatorial optimization models, and so on. Based on the idea of increasing intelligence, the algorithms are teased from three dimensions, namely, from the conventional exact algorithms, to metaheuristic algorithms, and finally to highly intelligent machine learning algorithms. Finally, the research proposes that LSISMP is expected to develop towards the autonomous modeling and the deep integration of machine learning and intelligent optimization algorithms.