{"title":"利用模型辅助和关注机制实现宽带二维多输入多输出雷达成像的稀疏解耦成像网络","authors":"Shanshan Ding, Zhijin Wen, Yang Liu, Nana Fan","doi":"10.1049/ell2.70000","DOIUrl":null,"url":null,"abstract":"<p>The problem of sparse decoupling radar imaging methods based on deep learning is researched. An improved model-driven learning imaging network with a complex-valued convolution block attention module plugged into each sub-network is proposed. This method can solve the high sidelobe and coupling problem in sparse wideband Multiple-Input Multiple-Output (MIMO) radar. In addition, it can better focus on the target area and capture target information to boost model representation power. Experimental results verify the validity of the proposed method.</p>","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70000","citationCount":"0","resultStr":"{\"title\":\"Sparse decoupling imaging network for wideband two-dimensional MIMO radar imaging using model-assisted and attention mechanism\",\"authors\":\"Shanshan Ding, Zhijin Wen, Yang Liu, Nana Fan\",\"doi\":\"10.1049/ell2.70000\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The problem of sparse decoupling radar imaging methods based on deep learning is researched. An improved model-driven learning imaging network with a complex-valued convolution block attention module plugged into each sub-network is proposed. This method can solve the high sidelobe and coupling problem in sparse wideband Multiple-Input Multiple-Output (MIMO) radar. In addition, it can better focus on the target area and capture target information to boost model representation power. Experimental results verify the validity of the proposed method.</p>\",\"PeriodicalId\":11556,\"journal\":{\"name\":\"Electronics Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70000\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electronics Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/ell2.70000\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronics Letters","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/ell2.70000","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Sparse decoupling imaging network for wideband two-dimensional MIMO radar imaging using model-assisted and attention mechanism
The problem of sparse decoupling radar imaging methods based on deep learning is researched. An improved model-driven learning imaging network with a complex-valued convolution block attention module plugged into each sub-network is proposed. This method can solve the high sidelobe and coupling problem in sparse wideband Multiple-Input Multiple-Output (MIMO) radar. In addition, it can better focus on the target area and capture target information to boost model representation power. Experimental results verify the validity of the proposed method.
期刊介绍:
Electronics Letters is an internationally renowned peer-reviewed rapid-communication journal that publishes short original research papers every two weeks. Its broad and interdisciplinary scope covers the latest developments in all electronic engineering related fields including communication, biomedical, optical and device technologies. Electronics Letters also provides further insight into some of the latest developments through special features and interviews.
Scope
As a journal at the forefront of its field, Electronics Letters publishes papers covering all themes of electronic and electrical engineering. The major themes of the journal are listed below.
Antennas and Propagation
Biomedical and Bioinspired Technologies, Signal Processing and Applications
Control Engineering
Electromagnetism: Theory, Materials and Devices
Electronic Circuits and Systems
Image, Video and Vision Processing and Applications
Information, Computing and Communications
Instrumentation and Measurement
Microwave Technology
Optical Communications
Photonics and Opto-Electronics
Power Electronics, Energy and Sustainability
Radar, Sonar and Navigation
Semiconductor Technology
Signal Processing
MIMO