一种基于六边形地理网格的遥感卫星覆盖范围 预测方法

To address the preprocessing requirements of imaging mission planning for large-scale remote sensing constellations，and solve the problems of sharply increased target visibility computation and time-consuming target screening caused by numerous satellite nodes and massive tasks，a sub-satellite point coverage prediction method based on four-aperture hexagonal geographic grid is proposed. First，grid subdivision and encoding rules are designed. The gnomonic projection is adopted to map the Earth’s surface onto a regular octahedron or regular icosahedron，and recursive subdivision is carried out with hexagons as basic units. Compared with the latitude-longitude grid，the proposed grid possesses better global uniformity，and its encoding principle supports vector operations among grids. On this basis， considering the condition that the sub-satellite point trajectory spans multiple polyhedral planes， a grid-based representation method for sub-satellite point trajectory is proposed. A grid recursion strategy is designed to replace the orbital dynamics integration process within short time intervals. By means of segmented trajectory prediction，the grid set covered by sub-satellite points in the planning cycle is obtained. Comparative simulation experiments show that the proposed grid recursion-based sub-satellite point trajectory prediction method improves computational efficiency by more than 10 times with a prediction error less than 7 km. Meanwhile，the grid-based satellite coverage prediction method increases the target screening efficiency by over 20 times，with a ground task screening accuracy higher than 98%. The method is well applicable to the preprocessing of mission planning for large-scale imaging constellations.