The mission planning model and improved Ant Colony solving algorithm for networking SAR satellites

As another important method to obtain geo-space information, synthetic aperture radar (SAR) has irreplaceable role in contrast to the visible light satellite, which plays an important part in economic construction, national defense and other fields. However, the research about networking SAR faces the problems of more user tasks, more complicated scheduling and wider solution space. This paper mainly expounds two respects of work: firstly, constructing multi-objective optimization model to complete the collaborative planning of networking SAR; secondly, combing the Ant Colony and Simulated Annealing to solve the model above which belongs to high dimensional discrete combinatorial optimization problems, so as to improve the convergence rate and robustness of the algorithm, the distributed simulation experiments show that it can increase 1.043% compared with Generic Algorithm, and 0.212% compared with Ant Colony in terms of synthesize factor.