Robust Adaptive Beamforming Based on Subspace Decomposition, Steering Vector Estimation and Correction

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Jian Yang;Yuwei Tu;Jian Lu;Zhiwei Yang
{"title":"Robust Adaptive Beamforming Based on Subspace Decomposition, Steering Vector Estimation and Correction","authors":"Jian Yang;Yuwei Tu;Jian Lu;Zhiwei Yang","doi":"10.1109/JSEN.2022.3174848","DOIUrl":null,"url":null,"abstract":"Considering that the performance of adaptive arrays is sensitive to any type of mismatches, an innovative robust adaptive beamforming method based on covariance matrix reconstruction, subspace decomposition, steering vector estimation and correction is proposed. Based on Capon spatial spectrum, a group of angle sets containing all interfering signals are determined, and the interference covariance matrix can be reconstructed with a smaller integration interval. On the other hand, the sample covariance matrix can be decomposed into signal subspace and interference-plus-noise by using the principle of maximum correlation. Based on the interference-plus-noise subspace and the reconstructed signal-plus-noise covariance matrix, a new convex optimization model is built to estimate the steering vector of the desired signal. Then, an improved projection approach based on signal subspace is designed for correction to improve the robustness against the nominal direction vector mismatches. Simulation results demonstrate that the proposed method achieves better overall performance under multiple mismatches over a wide range of input signal-to-noise ratios.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"22 12","pages":"12260-12268"},"PeriodicalIF":4.3000,"publicationDate":"2022-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/9775699/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 7

Abstract

Considering that the performance of adaptive arrays is sensitive to any type of mismatches, an innovative robust adaptive beamforming method based on covariance matrix reconstruction, subspace decomposition, steering vector estimation and correction is proposed. Based on Capon spatial spectrum, a group of angle sets containing all interfering signals are determined, and the interference covariance matrix can be reconstructed with a smaller integration interval. On the other hand, the sample covariance matrix can be decomposed into signal subspace and interference-plus-noise by using the principle of maximum correlation. Based on the interference-plus-noise subspace and the reconstructed signal-plus-noise covariance matrix, a new convex optimization model is built to estimate the steering vector of the desired signal. Then, an improved projection approach based on signal subspace is designed for correction to improve the robustness against the nominal direction vector mismatches. Simulation results demonstrate that the proposed method achieves better overall performance under multiple mismatches over a wide range of input signal-to-noise ratios.
基于子空间分解、转向矢量估计和校正的鲁棒自适应波束形成
针对自适应阵列对任何类型失配都很敏感的特点,提出了一种基于协方差矩阵重构、子空间分解、转向矢量估计和校正的鲁棒自适应波束形成方法。基于Capon空间谱,确定一组包含所有干扰信号的角度集,以较小的积分间隔重构干扰协方差矩阵。另一方面,利用最大相关原理将样本协方差矩阵分解为信号子空间和干扰加噪声。基于干扰加噪声子空间和重构的信号加噪声协方差矩阵,建立了新的凸优化模型来估计期望信号的转向向量。然后,设计了一种改进的基于信号子空间的投影方法进行校正,以提高对标称方向矢量失配的鲁棒性。仿真结果表明,在大范围的输入信噪比下,该方法在多种失配情况下取得了较好的综合性能。
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
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信