Efficient gridless wideband sparse array synthesis with tapped delay-lines

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2024-11-26 DOI:10.1016/j.dsp.2024.104893
Wenjing Zhou , Mingwei Shen , Di Wu , Daiyin Zhu , Guodong Han
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Abstract

In this paper, we propose a new wideband sparse array synthesis method based on gridless compressed sensing to solve the basis mismatch problem for discrete grids. Considering the tapped delay-lines (TDL) structure for space-time domain processing, and using successive frequency-varying atoms for sparse representation of wideband signals, an arbitrary sampling-atomic norm minimization is introduced to model the group sparsity-constrained wideband arrays in which the positions and the excitation values of array element are obtained with a high freedom. The above nonconvex problem is then transformed into a convex relaxation, which is solved using the Prolate Spheroidal Wave Functions (PSWFs). The experimental results show that the proposed sparse array design has higher matching accuracy and sparsity, compared with the discretized wideband sparse array design, which verifies the effectiveness and efficiency of this method.
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带抽头延迟线的高效无网格宽带稀疏阵列合成
针对离散网格的基错配问题,提出了一种基于无网格压缩感知的宽带稀疏阵列综合方法。利用抽头延迟线(TDL)结构进行空时域处理,利用连续变频原子对宽带信号进行稀疏表示,采用任意采样原子范数最小化方法对群稀疏约束的宽带阵列进行建模,使阵元的位置和激励值具有较高的自由度。然后将上述非凸问题转化为凸松弛问题,并利用长球面波函数(PSWFs)求解。实验结果表明,与离散化宽带稀疏阵列设计相比,本文提出的稀疏阵列设计具有更高的匹配精度和稀疏度,验证了该方法的有效性和高效性。
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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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