遥感卫星大范围区域观测在地图绘制、灾害救援等领域均具有重要作用，SAR遥感卫星具有不受云雾夜间环境影响的特点，研究SAR多星协同区域观测技术具有重要意义。本文首先对大范围复杂区域覆盖率计算进行分析，提出了结合高斯投影、网格划分与几何运算的复杂区域覆盖率计算方法；然后对SAR条带成像模式进行覆盖分析，提出了结合角度限制和二维分解的候选区域分解方法；最后提出了结合贪婪算法初始化、精英保留策略和三次适应度函数的改进遗传算法用于区域覆盖优化。本文选取四颗在轨SAR卫星和三个区域目标进行仿真实验，实验结果证明所提方法在北京、天津、上海三个区域目标上都能够实现优异的区域覆盖优化结果，相比贪婪算法，覆盖率分别提升3.17%、2.94%、9.02%。该算法可为SAR多星协同区域观测系统的建立提供技术基础。

Large-scale regional observations by remote sensing satellites play an important role in mapping, disaster relief and other fields. The efficiency of single satellite observation is low, and multi-satellite collaborative observation is the main means to solve the problem of rapid observation in large areas. At present, multi-satellite collaborative observation is mostly studied on optical remote sensing satellites, but there is less research on collaborative observation of SAR satellites. Moreover, SAR satellites have different imaging mechanisms and imaging modes from optical satellites, so the optical satellite collaborative planning method cannot be fully applied to SAR satellites. In order to give full play to the performance of SAR multi-satellite collaborative observation, it is urgent to study SAR multi-satellite collaborative regional observation technology. First of all, this study analyzes the coverage calculation of large-scale complex areas, and proposes a complex area coverage calculation method that combines Gaussian projection, grid division and geometric operations, which can realize coverage calculation of any complex area. Then, an accurate coverage analysis was performed on the SAR strip imaging mode, and a candidate area decomposition method combining angle restriction and two-dimensional decomposition was proposed. The optimization space is reduced through angle restriction to improve optimization efficiency, and complex continuous optimization problems are discretized through two-dimensional decomposition, making it possible to apply genetic algorithms for optimization. Finally, an improved genetic algorithm combining greedy algorithm initialization, elite retention strategy and cubic fitness function is proposed for regional coverage optimization. Chromosome encoding, crossover, and mutation operations are designed for optimization. The elite retention strategy is used to improve the optimization speed and stability, and the cubic fitness function is used to improve the optimization effect. This study selected four on-orbit SAR satellites: Gaofen-301, Gaofen-302, Gaofen-303, and Haisi-1, and three regional targets: Beijing, Tianjin, and Shanghai for simulation experiments. The simulation time is 5 days, the orbit data uses real TLE data, the SGP4 orbit propagation model is used, and the beam parameters of the SAR satellite are reasonably simulated. Experimental results show that the coverage rate of the proposed method on three regional targets in Beijing, Tianjin, and Shanghai is increased by 3.17%, 2.94%, and 9.02% respectively compared with the greedy algorithm. In the case of finer grids, the coverage result of the proposed method in the Shanghai area is 7.3% higher than that of the greedy algorithm. This study analyzes the SAR multi-satellite collaborative complex area coverage planning technology, constructs a feasible SAR multi-satellite collaborative complex area observation planning process, and proposes a complex area coverage planning method suitable for the SAR multi-satellite strip imaging mode. This algorithm can provide a technical basis for the establishment of a SAR multi-satellite collaborative regional planning system. However, the proposed method simplifies the constraints at the imaging signal processing level of SAR satellites, and subsequent research will conduct in-depth research based on the characteristics of SAR satellite imaging signal processing.