基于遗传算法的寻优解卫星星座优化设计策略

Optimal Design Strategy of Satellite Constellation Based on Genetic Algorithm

关键词:  星座优化设计  卫星星座构型  遗传算法

Key words:  Constellation optimization design  Satellite constellation configuration  Genetic algorithm

摘要:

卫星星座优化设计与直接部署卫星或普通迭代计算后部署卫星等方法不同，目的是在有限的资源中达到更好的星座观测效果。通过使用遗传算法（Genetic Algorithm），在卫星星座构型模型的基础上，得到星座种群内对目标观测实效性较强、重访周期较短的卫星个体，并使用较优的卫星个体生成Walker星座组网，实现了生成的星座对目标区域的高精度观测与覆盖。这种方法避免了复杂的计算与主观上的加权计算，在经济成本和观测效果相互制约的前提下，得到了更加高效的卫星星座构型设计策略。将此优化设计策略用于选定的卫星星座构型中，通过仿真实验表明，基于遗传算法优化后的“深圳一号”卫星星座相较于其优化前的部署，其对目标区域及全球区域的整体观测性能提升了90%以上。

Abstract:

Satellite constellation optimization design is different from methods such as direct deployment of satellites or deployment of satellites after ordinary iterative calculations, and the purpose is to achieve better constellation observation effects in limited resources. Through the use of genetic algorithm, based on the satellite constellation configuration model, the satellites in the constellation that are more effective in target observation and have a short revisit period are obtained, and the optimal satellites are used to generate the Walker constellation network, which achieves high-precision observation and coverage of the target area by the results. The method avoids complicated calculations and subjective weighting calculations, and obtains a more efficient satellite constellation configuration design strategy under the premise of mutual constraints on economic costs and observation effects. By using this optimization design strategy in the selected satellite constellation configuration, compared with the deployment before optimization, the optimized “Shenzhen-1” satellite constellation improves the overall observation performance of the target area and the global region by 90%.