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Phased array shaped multi-beam optimization for LEO satellite communications using a genetic algorithm

机译:使用遗传算法对Leo卫星通信进行相位阵列的多光束优化

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LEO Communications satellite antennas may require hundreds of high gain beams to achieve sufficient link margin, especially for mobile systems where the ground terminals have very low EIRP. Generally, the procedure for optimizing antenna beams for shaped coverage areas starts with a set of polygons defined in antenna angle space. These polygons are filled with synthesis stations at which the desired gain is prescribed. An optimization program is then used to synthesize the excitation of the antenna in order to achieve the desired gain at each station. For multibeam coverages, if the number and size of the coverage polygons are not optimal, pattern performance will be poor. Layout is difficult at LEO because a large variation in cell size is dictated by the substantial path length variation from nadir to edge of coverage. A genetic algorithm was developed to optimize the number and size of cells for a circularly symmetric grid. Cells were then filled with synthesis stations and a least squares optimizer used to shape the antenna pattern for each cell. A phased array antenna with circular aperture and (cosθ){sup}1.3 element power pattern was used. The genetic optimizer was found to quickly produce optimal cell layouts for arbitrary altitude, field of view, and directivity requirements. It was also a very good way to quickly and accurately determine the number of beams needed for a particular set of requirements.
机译:Leo通信卫星天线可能需要数百个高增益光束来实现足够的链接余量,特别是对于接地端子具有非常低的EIRP的移动系统。通常,用于优化用于成形覆盖区域的天线波束的过程从天线角度空间中定义的一组多边形开始。这些多边形填充有所需增益的合成站。然后使用优化程序来合成天线的激励,以便在每个站达到所需的增益。对于多波束覆盖范围,如果覆盖多边形的数量和大小不是最佳的,则图案性能将是差的。 Leo难以在Leo中难以,因为细胞尺寸的大变化由Nadir到覆盖边缘的大规模路径长度变化决定。开发了一种遗传算法以优化圆对称网格的单元的数量和大小。然后用合成站填充细胞和用于为每个电池塑造天线图案的最小二乘优化器。使用具有圆形孔径和(COSθ){sup} 1.3元件功率模式的相控阵天线。发现遗传优化剂快速生产用于任意高度,视野和方向性要求的最佳细胞布局。这也是一种非常好的方法,可以快速准确地确定特定要求所需的光束数量。

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