面向区域成像任务的环月卫星侧摆角优化方法

Optimization of Lateral Swing Angles of Lunar Satellite for Region Multiple Strip Imaging Task Planning

面向月表大区域成像任务需求,提出了拼接成像侧摆角优化方法。针对区域覆盖率计算,提出了基于矢量多边形逻辑运算的成像覆盖率快速计算方法,可在保证计算精度的同时降低计算复杂度;构建拼接成像任务侧摆角优化模型,采用基于sigmoid函数的自适应遗传算法求解;并采用两组仿真数据验证了该优化算法的可行性。

The lunar surface topography mapping is the basic work of the lunar exploration and plays an important role in the three-phase("around-fall-back") lunar exploration program. In the "around" phase, it is necessary to use the camera carried by the spacecraft to acquire high-resolution image of area around the drop-off point. Due to the low orbit altitude and the limited field of view of camera, the width of the imagery obtained by the lunar satellite is relatively small compared to the range of the required drop-off area. Multiple strips stitching is an effective and practical way to solve large regional imaging problem. In a given multiple strips stitching task planning, it is required to optimize the side-swing angle of each orbit within a given mission duration to meet the regional coverage demand. To meet the requirements of region multiple strip stitched imaging task,an lateral swing optimization of method is proposed in this paper. Firstly, a fast algorithm based on vector polygon logical operation is proposed. Coverage polygons of each imaging strip are calculated based on the lateral swing angle, satellite orbit and the field of view of the sensor, then the coverage ratio can be computed via a Boolean operation between target polygon and strip coverage polygons. The proposed method of coverage ratio calculation can not only ensure the accuracy of calculation, but also can dramatically reduce its computation complexity. Secondly, a lateral swing angle optimization model for multiple strip stitching imaging task is introduced, which uses swing angle as decision valuables, coverage ration maximization as objective function respectively, and an improved adaptive genetic algorithm based on sigmoid function is proposed to solve the optimization model. Finally, two simulation experiments are executed to validate the validity of proposed method.