A Novel Hybrid Mission Planning Method Based on Multilevel Icosahedral Mesh Partitioning

Interferometric Synthetic Aperture Radar (InSAR) satellite system has a widespread application for its high resolution, strong penetration, and all-weather observation capabilities. This research contributes to improving satellite access accuracy, visible window calculation, global mesh generation, and mission planning strategies. Firstly, an analytical high-precision earth observation model considering comprehensive perturbation is established to quickly obtain the visible window of the regional mission. Secondly, the multilevel icosahedron is introduced to generate the global meshes. Compared with the traditional method of equal latitude and longitude division, the distribution of covering sampling points has better consistency in both low and high latitudes, thus reducing the overlap of observation bands and calculation amount in the high latitudes. Furthermore, to solve the problems of low resource utilization and long planning period, an optimal coverage method of hybrid ascending and descending orbits is proposed. Meanwhile, the Greedy Genetic Algorithm (GGA) is designed for mission planning to efficiently obtain spatiotemporal sequences under complicated spaceborne resource constraints. Finally, the proposed method is applied to practical observation requirements of the LT-1 SAR satellite differential deformation measurement system. The experimental results show that it can significantly improve mission planning efficiency and regional coverage time.