用于二维DOA和偏振估计的改进型圆柱位移质素共形阵

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Mingcheng Fu , Zhi Zheng , Wen-Qin Wang
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引用次数: 0

摘要

与均匀共形阵列相比,稀疏共形阵列由于具有增大阵列孔径、提高自由度和减少相互耦合等优点而受到广泛关注。在本文中,我们设计了一种新的稀疏共形阵列,称为改进的圆柱位移质共形阵列(ICDCCA),用于二维(2-D)到达方向(DOA)和偏振估计。与现有的稀疏共形阵列不同,ICDCCA由多个双平行线性阵列组成,该阵列通过在原型协素数阵列中平行置换一个子阵列并扩大其传感器间间距来构建。对于ICDCCA构型,传感器位置有封闭表达式,可实现的自由度数可以解析计算,从而得到最优构型。与现有的稀疏共形阵列相比,该阵列具有更大的阵列孔径、更高的自由度和更小的相互耦合效应。数值结果表明,该方法优于现有的几种稀疏共形阵列。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved cylindrical displaced coprime conformal array for 2-D DOA and polarization estimation
Recently, sparse conformal arrays have received lots of attention due to its increased array aperture, improved degrees-of-freedom (DOFs) and reduced mutual coupling compared to uniform conformal arrays. In this article, we devise a new sparse conformal array, referred to as the improved cylindrical displaced coprime conformal array (ICDCCA) for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation. Unlike the existing sparse conformal arrays, the ICDCCA consists of multiple two-parallel linear arrays, which are constructed by displacing in parallel one subarray in the prototype coprime array and enlarging its inter-sensor spacings. For the ICDCCA configuration, there are closed-form expressions for the sensor positions, and its number of achievable DOFs can be analytically computed, from which the optimum configurations are obtained. Compared with the existing sparse conformal arrays, the proposed array configuration provides a larger array aperture, a higher number of DOFs, as well as less mutual coupling effects. Numerical results demonstrate the superiority of the ICDCCA over several existing sparse conformal arrays.
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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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