A High Degree of Freedom Radiation Near-Field Source Localization Algorithm with Gain–Phase Error

IF 1.4 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Sensors Pub Date : 2024-01-23 DOI:10.1155/2024/6834284
Qi Zhang, Wenxing Li, Si Li, Yunlong Mao
{"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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
具有增益相位误差的高自由度辐射近场源定位算法
本文研究了有增益相位误差的辐射近场源定位中可估计源数量的限制。当只有参考元素没有增益相位误差时,提出了一种基于精确模型的新方法,以提高可估计源的最大数量。根据辅助源的位置参数细节,该方法首先得出增益相位误差估计矩阵。其次,通过旋转不变性技术(TLS-ESPRIT)使用总最小平方估计信号参数来估计包含误差的信号源转向矢量,并利用时空组合思想建立时移数据矩阵,从而提高阵列的自由度。然后,根据增益相位误差估计矩阵构建的误差补偿矩阵对包含误差的源转向矢量进行修正。最后,利用修改后的声源转向矢量的增益相位和声源位置参数的闭合公式,得到声源位置参数的估计值。实验结果表明,当只有参考阵元没有增益相位误差时,与之前的结果相比,所提算法的最大可估算源数有了明显改善。当阵列数为 5 和 9 时,算法的最大可估计源数分别为 9 和 17。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Sensors
Journal of Sensors ENGINEERING, 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.
期刊最新文献
Energy-Efficient and Resilient Secure Routing in Energy Harvesting Wireless Sensor Networks with Transceiver Noises: EcoSecNet Design and Analysis A Passive Wireless Smart Washer for Locking Force Monitoring on the Orthopedic Pedicle Screw Modeling Forest Above-Ground Biomass of Teak (Tectona grandis L. F.) Using Field Measurement and Sentinel-2 Imagery Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control Platform Design of Passive Target Perception and Localization Based on Sensor Networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1