稀疏优化的宽带阵列farrow结构可变分数延迟滤波器

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC IET Signal Processing Pub Date : 2023-06-04 DOI:10.1049/sil2.12228
Wenjing Zhou, Mingwei Shen, Min Xu, Guodong Han, Yudong Zhang
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引用次数: 0

摘要

本文提出了一种新的稀疏性优化Farrow结构可变分数延迟(SFS-VFD)滤波器,以解决宽带阵列中的孔径效应。我们的方法基于系数(反)对称性,并优化其子滤波器的数量和阶数,大大减少了非零系数。建立的成本函数被公式化为具有多个正则化约束的参数最小化问题,并通过改进的三块交替方向乘法器方法(MTB-ADMM)求解,该方法通过引入核心变量校正项进行改进,以确保稳定快速的收敛。实验结果表明,SFS-VFD滤波器在保证高延迟精度的同时,减少了乘法器和加法器的使用,降低了系统的复杂性。在宽带阵列中,SFS-VFD滤波器有效地校正了孔径效应,实现了精确的波束指向。
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Sparsity-optimised farrow structure variable fractional delay filter for wideband array

In this paper, a new sparsity-optimised Farrow structure variable fractional delay (SFS-VFD) filter is proposed to address the aperture effect in wideband array. Our method is based on coefficient (anti-)symmetry and optimises the number and orders of its sub-filters, greatly reducing the non-zero coefficients. The established cost function is formulated as a parametric minimisation problem with multiple regularisation constraints, and solved by the modified three-block alternating direction multiplier method (MTB-ADMM), which is improved by introducing core variable correction items to ensure stable and fast convergence. Experimental results show that the SFS-VFD filter reduces the complexity of the system by decreasing the use of multipliers and adders while ensuring high delay accuracy. In wideband array, the SFS-VFD filter effectively corrects the aperture effect and achieves precise beam pointing.

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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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