巨型星座覆盖性的高效分析方法

Efficient algorithm for calculating coverage of mega-constellation

针对巨型星座设计及建设过程中的覆盖性分析问题,提出了一种高效覆盖性分析新方法:并行墨卡托投影图叠加法(PMPS)。该算法通过对墨卡托投影图的图像叠加处理,进行覆盖域计算,突破了传统网格法(GPA)中网格数量与星座卫星数量的关联约束,降低了计算消耗对卫星数量增长的敏感度。给定计算精度下,随着卫星数量的增加,GPA与PMPS计算消耗比值逐渐提高,并最终趋于极限值,该极限值和GPA选取网格量呈正相关。仿真结果表明;全球覆盖分析中GPA网格数为10;量级、满足指定计算误差时,未引入并行计算的PMPS效率相对GPA最大可提高1~2个量级,当GPA网格量更大时,如10;量级时,未引入并行计算的PMPS效率相对GPA预计最大可提高2~3个量级。

A new coverage analysis approach,Parallel Mercator Projection Superposition(PMPS),is proposed for the design and construction of mega-constellation.In this method,the image superposition is applied to the Mercator projection map of the constellation coverage area to conduct coverage calculation.The method breaks through the correlation constraint between the grid number and satellite number in the traditional Grid Point Approach(GPA),and significantly reduces the sensitivity of computing consumption to the satellites number.Given calculation accuracy,as the number of satellites increases,the ratio of PMPS calculation consumption to GPA’s gradually increases,and eventually tends to a limit value,which is positively correlated with the number of GPA grids.The simulation results show that in the global coverage analysis,when the number of GPA grids is 4 orders of magnitude and meets the specified calculation error,the efficiency of PMPS without parallel computing can be increased by 1 to 2 levels relative to that of GPA.When the number of GPA grids is larger,e.g.,at 5 orders of magnitude,the efficiency of PMPS without parallel computing is expected to be increased by 2 to 3 levels relative to that of GPA.