Bi-iterative MVDR Beamforming based on Beamspace Preprocessing for MIMO radars

Sheng Hong, J. Li, Y. Ai, Yantao Dong, Zhixin Zhao, Yuhao Wang
{"title":"Bi-iterative MVDR Beamforming based on Beamspace Preprocessing for MIMO radars","authors":"Sheng Hong, J. Li, Y. Ai, Yantao Dong, Zhixin Zhao, Yuhao Wang","doi":"10.1109/ISAPE.2018.8634139","DOIUrl":null,"url":null,"abstract":"In this paper, a bi-iterative minimum-variance distortionless response (MVDR) beamforming technique based on the beamspace preprocessing is proposed to solve two typical problems of the traditional MVDR beamformers in multi-input multi-output (MIMO) radars. Firstly, the omnidirectional power is radiated by traditional MIMO radars, however, the interested target is usually located in a certain spatial area. The unnecessary radiation on other areas will reduce the output signal-to-interference-plus-noise ratio (SINR) of the MVDR beamformer. To solve this problem, a transmit beamspace preprocessing by second order cone programming (SOCP) is utilized to focus the energy in the interested region. Secondly, the MVDR beamformer in MIMO radars usually requires a great number of training samples and very high computation load, since both the transmitted degrees of freedom (DoF) and the received DoF are fully utilized. To tackle with these problems, a bi-iterative MVDR beamforming algorithm is proposed based on the beamspace preprocessing. Simulation results demonstrate the efficiency of the proposed beamforming algorithm.","PeriodicalId":297368,"journal":{"name":"2018 12th International Symposium on Antennas, Propagation and EM Theory (ISAPE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 12th International Symposium on Antennas, Propagation and EM Theory (ISAPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISAPE.2018.8634139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In this paper, a bi-iterative minimum-variance distortionless response (MVDR) beamforming technique based on the beamspace preprocessing is proposed to solve two typical problems of the traditional MVDR beamformers in multi-input multi-output (MIMO) radars. Firstly, the omnidirectional power is radiated by traditional MIMO radars, however, the interested target is usually located in a certain spatial area. The unnecessary radiation on other areas will reduce the output signal-to-interference-plus-noise ratio (SINR) of the MVDR beamformer. To solve this problem, a transmit beamspace preprocessing by second order cone programming (SOCP) is utilized to focus the energy in the interested region. Secondly, the MVDR beamformer in MIMO radars usually requires a great number of training samples and very high computation load, since both the transmitted degrees of freedom (DoF) and the received DoF are fully utilized. To tackle with these problems, a bi-iterative MVDR beamforming algorithm is proposed based on the beamspace preprocessing. Simulation results demonstrate the efficiency of the proposed beamforming algorithm.
基于波束空间预处理的MIMO雷达双迭代MVDR波束形成
针对传统MVDR波束形成技术在多输入多输出(MIMO)雷达中存在的两个典型问题,提出了一种基于波束空间预处理的双迭代最小方差无失真响应(MVDR)波束形成技术。首先,传统MIMO雷达发射的是全向功率,而感兴趣的目标通常位于一定的空间区域内。在其他区域不必要的辐射会降低MVDR波束形成器的输出信噪比。为了解决这一问题,利用二阶锥规划(SOCP)对发射波束空间进行预处理,将能量集中在感兴趣的区域。其次,MIMO雷达中的MVDR波束形成器由于需要充分利用发射自由度和接收自由度,通常需要大量的训练样本和很高的计算负荷。为了解决这些问题,提出了一种基于波束空间预处理的双迭代MVDR波束形成算法。仿真结果验证了所提波束形成算法的有效性。
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
自引率
0.00%
发文量
0
×
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