基于压缩感知的多元非周期阵列合成

C. Bencivenni, M. Ivashina, R. Maaskant
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引用次数: 9

摘要

近年来,压缩感知在包括稀疏阵列天线合成在内的天线和电磁学的各个领域受到了广泛的关注。阵列的CS合成比解析方法具有更高的精度,并且允许大型复杂阵列的快速和确定性设计,而无需求助于计算昂贵的全局优化方法。本文提出的CS方法先前已被作者研究用于在相互耦合效应,波束扫描退化以及设计模块化的对称性的存在下设计最大稀疏阵列。在本文中,作者展示了这种方法的另一种(尚未探索的)能力,即在阵列合成过程中合并不同的元素类型并确定其最佳组合。文中给出了在卫星通信多波束场景下由均匀圆孔径元件组成的大型阵列的数值算例。结果表明,利用这种能力,可以同时减少元件数量和增加扫描损耗。
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Multi-element aperiodic array synthesis by Compressive Sensing
In recent years, Compressive Sensing has attracted considerable attention in various areas of antennas and electromagnetics, including the synthesis of sparse array antennas. The CS synthesis of arrays achieves higher accuracy than analytical methods and allows for the fast and deterministic design of large complex arrays, without resorting to computationally expensive Global Optimization methods. The CS approach presented here has been previously studied by the authors for the design of maximally sparse arrays in the presence of mutual coupling effects, beam scanning degradation, as well as the imposition of symmetries for design modularity. In this manuscript the authors demonstrate another (yet unexplored) capability of such an approach, i.e., to incorporate different element types and determine their optimum combination in the course of the array synthesis procedure. Numerical examples are illustrated for large arrays comprising uniform circular aperture elements and operating in a SATCOM multi-beam scenario. It is shown that by exploiting this capability it is possible to simultaneously reduce the number of elements and gain scan loss.
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