Abstract

Considering the increasing demand for earth observation missions, aiming at the centralized cooperative mission planning problem of remote sensing satellites, analyzing the constraints in the operation of satellites while considering the load and platform operation, and establishing a reasonable mathematical calculation of satellite missions model. The population incremental learning (PBIL) algorithm is used to solve the satellite mission planning problem. The binary coding method of the traditional PBIL algorithm is improved to the real coding method, and the value matrix correction method is improved. The computational efficiency of PBIL algorithm based on real number coding is verified by numerical examples. The performances of genetic algorithm and PBIL algorithm in solving satellite mission planning problems are compared and analyzed.

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