基于离散点访问信息聚类的区域覆盖规划算法

Area Coverage Planning Algorithm Based on Access Information Clustering of Discrete Points

摘要:

区域覆盖是遥感卫星领域的重要内容，常规条带覆盖方法效率不高，适应性不强。本研究旨在解决遥感卫星任务规划中，多颗遥感卫星在一定时间内对指定区域进行最大化覆盖的问题。根据所有卫星的最小观测幅宽，对待观测区域进行离散化处理，依据访问时间和访问角度等信息进行聚类处理，筛选出最优观测方案，优化卫星观测顺序、侧摆角度等观测任务安排。研究结果表明，该算法对不同区域目标实现成像覆盖时，所需成像条带数量更少，覆盖率更高。避免了因卫星观测幅宽、轨道方向不同带来的问题复杂性，对不同区域类型、不同卫星类型均具有较好的适应能力，解决了遥感卫星任务规划领域中多颗遥感卫星在一定时间内对指定区域进行最大化覆盖的问题。

Abstract:

Regional coverage is of great significance in satellite remote sensing. The conventional strip coverage method, however, is inefficient and has poor adaptability. This study aims to solve the problem of maximizing the coverage of a specified area by multiple remote - sensing satellites within a given time in satellite remote - sensing mission planning. It discretizes the area to be observed based on the minimum observation width of all satellites, conducts clustering using access - related information, selects the optimal observation plan, and optimizes observation tasks. Results show that the algorithm requires fewer imaging strips and achieves a higher coverage rate for different regional targets. It can avoid complex problems caused by satellite - related differences, and thus resolves the coverage - maximization issue.