非圆信号DOA估计的扩展翻转CADiS阵列配置

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing Pub Date : 2025-05-12 DOI:10.1016/j.dsp.2025.105318

Xiaolong Li , Xiaofei Zhang , Hao Hu

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引用次数: 0

摘要

具有位移子阵（CADiS）结构的互素阵在减少相互耦合和提高自由度方面具有显著的优势。然而，将CADiS应用于非圆（NC）信号的DOA估计时，由于差分共阵（DCA）和和共阵（SCA）之间的重叠以及空穴的存在，可能会导致估计精度下降。为了提高数控信号的DOA估计精度，本文提出了一种扩展的翻转CADiS （efCADiS）阵列结构。具体而言，我们首先翻转cadi的两个子阵列，以减少和差共阵列（SDCA）的重叠段。接下来，通过添加一个额外的传感器来填补SDCA中的漏洞，我们扩展了SDCA并引入了efCADiS。最后，我们导出了efCADiS的最优子阵间距，以获得更均匀的自由度。理论和仿真结果验证了该方法在自由度、互耦性和估计性能等方面的优越性。

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Extended flipping CADiS array configuration for DOA estimation of non-circular signals

Coprime Array with Displaced Subarrays (CADiS) structure has significant advantages in reducing mutual coupling and increasing degrees of freedom (DOFs). However, when applying CADiS in DOA estimation of non-circular (NC) signals, degradation of estimation accuracy may occur due to the overlap between difference coarray (DCA) and sum coarray (SCA), as well as the presence of holes. In this paper, we propose an extended flipping CADiS (efCADiS) array configuration to enhance the DOA estimation accuracy of NC signals. Specifically, we first flip the two subarrays of CADiS to reduce the overlapping segment of sum-difference coarray (SDCA). Next, by adding an additional sensor to fill the holes in the SDCA, we extend the SDCA and introduce the efCADiS. Finally, we derive the optimal subarray spacing for efCADiS to achieve more uniform DOFs. The theoretical and simulation results have verified the superiority of the proposed efCADiS regarding DOF, mutual coupling and estimation performance.

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来源期刊

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,