{"title":"利用协方差矩阵变换和组合优化MIMO雷达发射波束形成","authors":"Elahe Faghand, Esfandiar Mehrshahi","doi":"10.1016/j.sigpro.2025.110312","DOIUrl":null,"url":null,"abstract":"<div><div>This article presents an efficient approach for the design of waveform covariance matrices in colocated MIMO radars to achieve desired transmit beampatterns. The proposed method, based on Unconstrained Quadratic Programming (UQP), synthesizes a covariance matrix once, and through straightforward transformations and combinations, a variety of single- and multilobe beampatterns can be generated. These transformations are computationally efficient, as they do not require solving the beampattern matching problem repeatedly. The approach ensures that the corresponding covariance matrices adhere to practical constraints while minimizing computational effort. We also demonstrate how this method can be applied to control mainlobe levels and create beampatterns for scenarios where the radar system experiences saturation and level control in the field of view is needed. The proposed method is validated through simulations and numerical result, where it shows superior performance in terms of MSE and computational time compared to existing methods for real-time radar applications.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110312"},"PeriodicalIF":3.6000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimized transmit beamforming via covariance matrix transformation and combination in colocated MIMO radars\",\"authors\":\"Elahe Faghand, Esfandiar Mehrshahi\",\"doi\":\"10.1016/j.sigpro.2025.110312\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This article presents an efficient approach for the design of waveform covariance matrices in colocated MIMO radars to achieve desired transmit beampatterns. The proposed method, based on Unconstrained Quadratic Programming (UQP), synthesizes a covariance matrix once, and through straightforward transformations and combinations, a variety of single- and multilobe beampatterns can be generated. These transformations are computationally efficient, as they do not require solving the beampattern matching problem repeatedly. The approach ensures that the corresponding covariance matrices adhere to practical constraints while minimizing computational effort. We also demonstrate how this method can be applied to control mainlobe levels and create beampatterns for scenarios where the radar system experiences saturation and level control in the field of view is needed. The proposed method is validated through simulations and numerical result, where it shows superior performance in terms of MSE and computational time compared to existing methods for real-time radar applications.</div></div>\",\"PeriodicalId\":49523,\"journal\":{\"name\":\"Signal Processing\",\"volume\":\"239 \",\"pages\":\"Article 110312\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-09-25\",\"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/S0165168425004268\",\"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/S0165168425004268","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 transmit beamforming via covariance matrix transformation and combination in colocated MIMO radars
This article presents an efficient approach for the design of waveform covariance matrices in colocated MIMO radars to achieve desired transmit beampatterns. The proposed method, based on Unconstrained Quadratic Programming (UQP), synthesizes a covariance matrix once, and through straightforward transformations and combinations, a variety of single- and multilobe beampatterns can be generated. These transformations are computationally efficient, as they do not require solving the beampattern matching problem repeatedly. The approach ensures that the corresponding covariance matrices adhere to practical constraints while minimizing computational effort. We also demonstrate how this method can be applied to control mainlobe levels and create beampatterns for scenarios where the radar system experiences saturation and level control in the field of view is needed. The proposed method is validated through simulations and numerical result, where it shows superior performance in terms of MSE and computational time compared to existing methods for real-time radar applications.
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.