Synthesis of maximally sparse conformal circular arc array with a required beam pattern by unitary matrix pencil method

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Bin Kong , Yongjun Li , Pengfei Zhao , Pin Wen , Foxiang Liu
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引用次数: 0

Abstract

This paper extends the unitary matrix pencil (UMP) method to synthesize maximally sparse conformal circular-arc array with a required beam pattern. Due to the nonlinearity between the circular-arc array pattern and its element pattern, Fourier transform preprocessing for the required beam pattern is introduced to achieve a mathematical expression, i.e., sum of a series of undamped complex exponentials, which is related to array element positions and their excitations. Then, the UMP method is used to determine the reduced number of elements and their position distributions. Moreover, the complex excitations of array elements are reconstructed by obtaining the least-square solution of an over-determined equation. A set of examples for synthesizing sparse conformal circular-arc arrays with different desired patterns and E-type patch element including the mutual coupling are conducted. Results show that the proposed UMP method can achieve a considerably lower pattern reconstruction error with a reduced number of elements than results in the literature, which demonstrates its effectiveness and robustness.

用单元矩阵铅笔法合成具有所需波束模式的最大稀疏共形圆弧阵列
本文扩展了单元矩阵铅笔(UMP)方法,以合成具有所需波束图案的最大稀疏共形圆弧阵列。由于圆弧阵列图案与阵元图案之间的非线性,本文引入了对所需波束图案的傅立叶变换预处理,以获得与阵元位置及其激励相关的数学表达式,即一系列无阻尼复指数之和。然后,使用 UMP 方法确定减少的元素数量及其位置分布。此外,阵列元素的复激励是通过获取超定方程的最小二乘法解来重建的。通过一组实例合成了具有不同所需图案的稀疏共形圆弧阵列和包括相互耦合在内的 E 型贴片元件。结果表明,与文献结果相比,所提出的 UMP 方法可以在减少元素数量的情况下实现更低的图案重建误差,这证明了它的有效性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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