Design of differential microphone array beampatterns with sidelobe level constraints

IF 3.4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Signal Processing Pub Date : 2024-10-22 DOI:10.1016/j.sigpro.2024.109748
Pu Zheng, Yongfeng Zhi
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Abstract

The target beampattern of differential microphone arrays (DMAs) often satisfies design requirements to optimize the performance of the beamformer by maximizing a specific advantage. This paper focuses on designing and implementing the minimum mainlobe width beampattern under the constrained sidelobe level. The main works are as follows. (1) We derive the minimum mainlobe width target beampattern under certain sidelobe level constraints from the Chebyshev-Type pattern that satisfies the sufficient conditions for effective target beampatterns. (2) We design a Jacobi–Anger expansion approximation differential beamforming filter for the Chebyshev-Type target beampattern to ensure that the resulting beampattern is consistent with the Chebyshev-type target beampattern and the beamformer’s robustness can be improved by using more microphones to obtain a minimum-norm solution. Compared with the conventional frequency-independent pattern Jacobi–Anger expansion method, the Chebyshev-Type Jacobi–Anger expansion beamformer we designed can flexibly limit the sidelobe level and obtain the minimum mainlobe beamwidth.
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设计具有边音电平限制的差分传声器阵列蜂鸣器
差分传声器阵列(DMA)的目标振型通常需要满足设计要求,通过最大限度地发挥特定优势来优化波束成形器的性能。本文的重点是设计和实现受限侧叶水平下的最小主叶宽度波束赋形。主要工作如下(1) 我们从满足有效目标贝型充分条件的切比雪夫型贝型推导出特定边瓣电平约束下的最小主边瓣宽度目标贝型。(2) 我们为切比雪夫型目标波束赋形设计了雅各比-安格尔扩展近似差分波束赋形滤波器,以确保得到的波束赋形与切比雪夫型目标波束赋形一致,并通过使用更多传声器来获得最小规范解,从而提高波束赋形器的鲁棒性。与传统的与频率无关的雅各比-安格尔扩展方法相比,我们设计的切比雪夫型雅各比-安格尔扩展波束成形器可以灵活地限制边瓣电平,并获得最小的主波束宽度。
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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