Saeid Sedighi , Nazila Karimian-Sichani , Bhavani Shankar M.R. , Maria S. Greco , Fulvio Gini , Björn Ottersten
{"title":"基于波束匹配的二维稀疏天线阵优化设计","authors":"Saeid Sedighi , Nazila Karimian-Sichani , Bhavani Shankar M.R. , Maria S. Greco , Fulvio Gini , Björn Ottersten","doi":"10.1016/j.sigpro.2025.110086","DOIUrl":null,"url":null,"abstract":"<div><div>Emerging millimeter-wave (mmWave) MIMO radars combine the benefits of large bandwidth available at mmWave frequencies with the spatial diversity provided by MIMO architectures, significantly enhancing radar capabilities for automotive, surveillance, and imaging applications. However, deploying large numbers of antennas and transceivers at these high frequencies substantially increases chip complexity and hardware costs. In this paper, we address the design of sparse two-dimensional (2D) antenna arrays that retain the desirable beampattern characteristics of fully populated arrays – namely, narrow mainlobes and low sidelobes – while significantly reducing the required number of antenna elements. We formulate the sparse array design problem as a beampattern matching optimization, which selects optimal subsets of transmit and receive antenna positions from an initial dense grid. To efficiently solve this challenging nonconvex optimization problem, we introduce an iterative algorithm combining Majorization–Minimization (MM) and Alternating Optimization (AO) techniques. We provide theoretical guarantees for convergence to at least a local optimum. Additionally, we propose a weighting vector optimization step to further enhance sidelobe suppression. Numerical simulations confirm that the proposed method maintains angular resolution and Sidelobe Levels (SLLs) comparable to those of full arrays, while substantially reducing hardware complexity and cost. Performance comparisons against existing methods demonstrate notable improvements in sidelobe suppression and computational efficiency without compromising processing gain.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"238 ","pages":"Article 110086"},"PeriodicalIF":3.4000,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimized sparse 2D antenna array design via beampattern matching\",\"authors\":\"Saeid Sedighi , Nazila Karimian-Sichani , Bhavani Shankar M.R. , Maria S. Greco , Fulvio Gini , Björn Ottersten\",\"doi\":\"10.1016/j.sigpro.2025.110086\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Emerging millimeter-wave (mmWave) MIMO radars combine the benefits of large bandwidth available at mmWave frequencies with the spatial diversity provided by MIMO architectures, significantly enhancing radar capabilities for automotive, surveillance, and imaging applications. However, deploying large numbers of antennas and transceivers at these high frequencies substantially increases chip complexity and hardware costs. In this paper, we address the design of sparse two-dimensional (2D) antenna arrays that retain the desirable beampattern characteristics of fully populated arrays – namely, narrow mainlobes and low sidelobes – while significantly reducing the required number of antenna elements. We formulate the sparse array design problem as a beampattern matching optimization, which selects optimal subsets of transmit and receive antenna positions from an initial dense grid. To efficiently solve this challenging nonconvex optimization problem, we introduce an iterative algorithm combining Majorization–Minimization (MM) and Alternating Optimization (AO) techniques. We provide theoretical guarantees for convergence to at least a local optimum. Additionally, we propose a weighting vector optimization step to further enhance sidelobe suppression. Numerical simulations confirm that the proposed method maintains angular resolution and Sidelobe Levels (SLLs) comparable to those of full arrays, while substantially reducing hardware complexity and cost. Performance comparisons against existing methods demonstrate notable improvements in sidelobe suppression and computational efficiency without compromising processing gain.</div></div>\",\"PeriodicalId\":49523,\"journal\":{\"name\":\"Signal Processing\",\"volume\":\"238 \",\"pages\":\"Article 110086\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0165168425002002\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165168425002002","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Optimized sparse 2D antenna array design via beampattern matching
Emerging millimeter-wave (mmWave) MIMO radars combine the benefits of large bandwidth available at mmWave frequencies with the spatial diversity provided by MIMO architectures, significantly enhancing radar capabilities for automotive, surveillance, and imaging applications. However, deploying large numbers of antennas and transceivers at these high frequencies substantially increases chip complexity and hardware costs. In this paper, we address the design of sparse two-dimensional (2D) antenna arrays that retain the desirable beampattern characteristics of fully populated arrays – namely, narrow mainlobes and low sidelobes – while significantly reducing the required number of antenna elements. We formulate the sparse array design problem as a beampattern matching optimization, which selects optimal subsets of transmit and receive antenna positions from an initial dense grid. To efficiently solve this challenging nonconvex optimization problem, we introduce an iterative algorithm combining Majorization–Minimization (MM) and Alternating Optimization (AO) techniques. We provide theoretical guarantees for convergence to at least a local optimum. Additionally, we propose a weighting vector optimization step to further enhance sidelobe suppression. Numerical simulations confirm that the proposed method maintains angular resolution and Sidelobe Levels (SLLs) comparable to those of full arrays, while substantially reducing hardware complexity and cost. Performance comparisons against existing methods demonstrate notable improvements in sidelobe suppression and computational efficiency without compromising processing gain.
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.