{"title":"A High Degree of Freedom Radiation Near-Field Source Localization Algorithm with Gain–Phase Error","authors":"Qi Zhang, Wenxing Li, Si Li, Yunlong Mao","doi":"10.1155/2024/6834284","DOIUrl":null,"url":null,"abstract":"The limitation of the number of estimable sources in the localization of radiation near-field sources with gain–phase error is examined in this paper. When only the reference element has no gain–phase error, a new method based on an accurate model is proposed to enhance the maximum number of estimable sources. Based on the location parameter details of the auxiliary source, the method first derives the gain–phase error estimate matrix. Second, the source steering vector including errors is estimated using the total least square estimating signal parameter via rotational invariance techniques (TLS-ESPRIT), and the time-shifted data matrix is built utilizing the space–time combination idea, thus increasing the degree of freedom of the array. Then, the source steering vector containing the error is modified by the error compensation matrix constructed according to the moment of gain–phase error estimation. Finally, the estimated values of the source position parameters are obtained by using the closed formula of the gain phase of the modified source steering vector and the source position parameters. The experimental results show that the maximum estimable source number of the proposed algorithm is significantly improved compared with the previous results when only the reference array element has no gain–phase error. When the array number is 5 and 9, the maximum estimable source number of the algorithm is 9 and 17, respectively.","PeriodicalId":48792,"journal":{"name":"Journal of Sensors","volume":"204 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Sensors","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1155/2024/6834284","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The limitation of the number of estimable sources in the localization of radiation near-field sources with gain–phase error is examined in this paper. When only the reference element has no gain–phase error, a new method based on an accurate model is proposed to enhance the maximum number of estimable sources. Based on the location parameter details of the auxiliary source, the method first derives the gain–phase error estimate matrix. Second, the source steering vector including errors is estimated using the total least square estimating signal parameter via rotational invariance techniques (TLS-ESPRIT), and the time-shifted data matrix is built utilizing the space–time combination idea, thus increasing the degree of freedom of the array. Then, the source steering vector containing the error is modified by the error compensation matrix constructed according to the moment of gain–phase error estimation. Finally, the estimated values of the source position parameters are obtained by using the closed formula of the gain phase of the modified source steering vector and the source position parameters. The experimental results show that the maximum estimable source number of the proposed algorithm is significantly improved compared with the previous results when only the reference array element has no gain–phase error. When the array number is 5 and 9, the maximum estimable source number of the algorithm is 9 and 17, respectively.
Journal of SensorsENGINEERING, ELECTRICAL & ELECTRONIC-INSTRUMENTS & INSTRUMENTATION
CiteScore
4.10
自引率
5.30%
发文量
833
审稿时长
18 weeks
期刊介绍:
Journal of Sensors publishes papers related to all aspects of sensors, from their theory and design, to the applications of complete sensing devices. All classes of sensor are covered, including acoustic, biological, chemical, electronic, electromagnetic (including optical), mechanical, proximity, and thermal. Submissions relating to wearable, implantable, and remote sensing devices are encouraged.
Envisaged applications include, but are not limited to:
-Medical, healthcare, and lifestyle monitoring
-Environmental and atmospheric monitoring
-Sensing for engineering, manufacturing and processing industries
-Transportation, navigation, and geolocation
-Vision, perception, and sensing for robots and UAVs
The journal welcomes articles that, as well as the sensor technology itself, consider the practical aspects of modern sensor implementation, such as networking, communications, signal processing, and data management.
As well as original research, the Journal of Sensors also publishes focused review articles that examine the state of the art, identify emerging trends, and suggest future directions for developing fields.