An Estimation Algorithm of Population Feature Distribution for the Imaging Constellation Task Planning Problem

An estimation algorithm of population feature distribution (EPFD) is proposed to improve the completion and imaging quality of imaging constellation observation tasks. Firstly, the observation task constraints and on-board resource constraints of agile imaging satellites are analyzed, the objective function of evaluating task completion and imaging quality is constructed, and the task planning model of agile imaging constellation is established. Then, the coding and decoding strategy with imaging time as the optimization object is designed. The concepts of global heuristic information and local heuristic information are given. A method for establishing the probability distribution model of imaging time based on heuristic information and a population evolution method are proposed. The directional search of imaging time is realized, and the completion of observation tasks and imaging quality are improved. Finally, the algorithm and particle swarm optimization algorithm are compared and analyzed through engineering examples. The results show that the algorithm has a fast convergence speed and a good optimization effect.