A novel adaptive beamforming for multipath signal reception based on eigenspace

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

Digital Signal Processing Pub Date : 2025-04-28 DOI:10.1016/j.dsp.2025.105282

Rongchen Sun, Zhaoyu Shi, Zhenduo Wang, Zengmao Chen, Zhiguo Sun

{"title":"A novel adaptive beamforming for multipath signal reception based on eigenspace","authors":"Rongchen Sun, Zhaoyu Shi, Zhenduo Wang, Zengmao Chen, Zhiguo Sun","doi":"10.1016/j.dsp.2025.105282","DOIUrl":null,"url":null,"abstract":"<div><div>This paper introduces a new beamforming method designed for receiving multipath signals. It addresses the issue of signal cancellation caused by multipath signals in traditional beamformers. First, a bearing response function based on eigenspace is developed for multipath scenarios. This function is utilized to estimate the composite steering vector (CSV). Because it effectively captures multipath fading coefficients. Subsequently, in a multipath environment, the applicability of the power estimation method in the signal subspace scaled multiple signal classification algorithm is analyzed. This method is used to estimate the power of uncorrelated interference. Finally, the CSV is combined with a reconstructed interference-plus-noise covariance matrix to derive the optimal weight vector, achieving reception of multipath signals. Numerical simulations verify the effectiveness and superiority of the proposed beamformer. However, the computational complexity of the proposed beamformer is relatively high.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"164 ","pages":"Article 105282"},"PeriodicalIF":2.9000,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1051200425003045","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

Abstract

This paper introduces a new beamforming method designed for receiving multipath signals. It addresses the issue of signal cancellation caused by multipath signals in traditional beamformers. First, a bearing response function based on eigenspace is developed for multipath scenarios. This function is utilized to estimate the composite steering vector (CSV). Because it effectively captures multipath fading coefficients. Subsequently, in a multipath environment, the applicability of the power estimation method in the signal subspace scaled multiple signal classification algorithm is analyzed. This method is used to estimate the power of uncorrelated interference. Finally, the CSV is combined with a reconstructed interference-plus-noise covariance matrix to derive the optimal weight vector, achieving reception of multipath signals. Numerical simulations verify the effectiveness and superiority of the proposed beamformer. However, the computational complexity of the proposed beamformer is relatively high.

查看原文本刊更多论文

基于特征空间的多径信号接收自适应波束形成

本文介绍了一种新的多径信号接收波束形成方法。它解决了传统波束形成器中多径信号产生的信号抵消问题。首先，针对多路径场景，建立了基于特征空间的方位响应函数。该函数用于估计复合转向向量（CSV）。因为它能有效地捕获多径衰落系数。随后，在多径环境下，分析了功率估计方法在信号子空间尺度多信号分类算法中的适用性。该方法用于估计不相关干扰的功率。最后，将CSV与重构的干涉加噪声协方差矩阵相结合，导出最优权向量，实现多径信号的接收。数值仿真验证了该波束形成器的有效性和优越性。然而，该波束形成器的计算复杂度相对较高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

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,