基于压缩感知的圆形等参稀疏阵列合成

C. Bencivenni, M. Ivashina, R. Maaskant
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引用次数: 5

摘要

一种基于压缩感知的大规模稀疏阵列设计方法最近在文献中被引入,并扩展到包括复杂的电磁效应和扫描性能。然而,这种方法不能直接控制激励幅值的数量。在这里,我们采用了一个两步的过程,首先使用迭代的1-范数最小化方法合成具有无约束振幅的连续环,然后将它们替换为具有与每个环的原始振幅成比例的元素的圆形等隐环阵列。该程序演示了一个各向同性阵列的10λ半径,其中参考解决方案基于解析密度-锥度方法是在文献中可用。结果表明,该方法能够在减少25%的元件或降低4dB的峰值旁瓣电平的情况下实现阵列孔径的显著减小(20%)。
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Synthesis of circular isophoric sparse arrays by using compressive-sensing
A design approach for large-scale sparse arrays based on Compressive Sensing has been recently introduced in the literature and extended to include complex EM effects and scan performance. However, that approach cannot directly control the number of excitation amplitudes. Here, we apply a two-step procedure that first synthesizes continuous rings with unconstrained amplitudes using an iterative ℓ1-norm minimization approach, and then replaces them with a circular isophoric ring array with a number of elements proportional to the original amplitude of each ring. The procedure is demonstrated for an isotropic array of a 10λ radius, for which a reference solution based on the analytical density-taper approach is available in the literature. Results show the capability of the proposed method to achieve a significant reduction of the array aperture (20%) with 25% less elements or 4dB lower peak side lobe level.
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