{"title":"Probability-Based Complex-Valued Fast Iterative Shrinkage-Thresholding Algorithm for Deconvolution Beamforming","authors":"Shiyao Jiang;Rongxin Jiang;Xuesong Liu;Boxuan Gu;Yaowu Chen","doi":"10.1109/JOE.2023.3339800","DOIUrl":null,"url":null,"abstract":"Conventional beamforming is widely used in sonars and radars owing to its robustness and low complexity; however, it suffers from low beam resolution and high-intensity sidelobes. Various imaging deblurring methods have been used in deconvolution beamforming to improve the beam resolution. A considerable limitation of these intensity-based methods is that the real-valued model mismatches the signals in practice and ignores the coherent information of the received signals. This study proposes a probability-based complex-valued fast iterative shrinkage-thresholding algorithm (CFISTA) to extend deconvolution beamforming to the complex domain. In this novel algorithm, the complex gradient descent and complex probability mapping are combined. Fast Fourier transform acceleration and clustering prior constraints are used on the targets to improve the efficiency and accuracy of the model. Simulation results of planar arrays show that the proposed method has superior beam resolution, sidelobe suppression, running time, and noise immunity compared with those of intensity-based methods.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"49 2","pages":"340-351"},"PeriodicalIF":3.8000,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Oceanic Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10436652/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
Abstract
Conventional beamforming is widely used in sonars and radars owing to its robustness and low complexity; however, it suffers from low beam resolution and high-intensity sidelobes. Various imaging deblurring methods have been used in deconvolution beamforming to improve the beam resolution. A considerable limitation of these intensity-based methods is that the real-valued model mismatches the signals in practice and ignores the coherent information of the received signals. This study proposes a probability-based complex-valued fast iterative shrinkage-thresholding algorithm (CFISTA) to extend deconvolution beamforming to the complex domain. In this novel algorithm, the complex gradient descent and complex probability mapping are combined. Fast Fourier transform acceleration and clustering prior constraints are used on the targets to improve the efficiency and accuracy of the model. Simulation results of planar arrays show that the proposed method has superior beam resolution, sidelobe suppression, running time, and noise immunity compared with those of intensity-based methods.
期刊介绍:
The IEEE Journal of Oceanic Engineering (ISSN 0364-9059) is the online-only quarterly publication of the IEEE Oceanic Engineering Society (IEEE OES). The scope of the Journal is the field of interest of the IEEE OES, which encompasses all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.