{"title":"基于电磁矢量传感器阵列的多目标直接定位","authors":"Ziheng Zhao, Rui Guo, Qi Liu, Shiyou Xu","doi":"10.1016/j.sigpro.2025.110292","DOIUrl":null,"url":null,"abstract":"<div><div>Electromagnetic vector sensors (EMVSs) have gained significant attention in recent years, particularly in the field of source localization. These multi-component sensors are capable of simultaneously detecting both electric and magnetic field vector information, making them a key area of research and development. Traditional source localization methods usually estimate the source position by estimating intermediate parameters such as direction of arrival (DOA) or time of arrival (TOA) first, which involves multiple processing steps and is highly susceptible to noise. This paper employs EMVS for direct position determination (DPD), proposing distinct algorithms for line-of-sight (LOS) and multipath scenarios. In the LOS scenario, the inherent multidimensional structure of the data received by the EMVS is utilized to represent the received signal as a third-order tensor. Using the selected dual-component EMVS in this paper, data from multiple stations are concatenated into a large tensor, and the spatial location parameters of the target source are directly extracted through parallel factor (PARAFAC) decomposition. In the NLOS scenario, the data received at each station are first decorrelated, followed by direct extraction of the target source’s spatial location parameters using PARAFAC decomposition. The proposed methods eliminate the need for explicit estimation of intermediate parameters, perform localization directly in the tensor domain, and exhibit strong robustness and high capability in resolving multiple sources.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"239 ","pages":"Article 110292"},"PeriodicalIF":3.6000,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Direct positioning of multiple targets based on electromagnetic vector sensors array\",\"authors\":\"Ziheng Zhao, Rui Guo, Qi Liu, Shiyou Xu\",\"doi\":\"10.1016/j.sigpro.2025.110292\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Electromagnetic vector sensors (EMVSs) have gained significant attention in recent years, particularly in the field of source localization. These multi-component sensors are capable of simultaneously detecting both electric and magnetic field vector information, making them a key area of research and development. Traditional source localization methods usually estimate the source position by estimating intermediate parameters such as direction of arrival (DOA) or time of arrival (TOA) first, which involves multiple processing steps and is highly susceptible to noise. This paper employs EMVS for direct position determination (DPD), proposing distinct algorithms for line-of-sight (LOS) and multipath scenarios. In the LOS scenario, the inherent multidimensional structure of the data received by the EMVS is utilized to represent the received signal as a third-order tensor. Using the selected dual-component EMVS in this paper, data from multiple stations are concatenated into a large tensor, and the spatial location parameters of the target source are directly extracted through parallel factor (PARAFAC) decomposition. In the NLOS scenario, the data received at each station are first decorrelated, followed by direct extraction of the target source’s spatial location parameters using PARAFAC decomposition. The proposed methods eliminate the need for explicit estimation of intermediate parameters, perform localization directly in the tensor domain, and exhibit strong robustness and high capability in resolving multiple sources.</div></div>\",\"PeriodicalId\":49523,\"journal\":{\"name\":\"Signal Processing\",\"volume\":\"239 \",\"pages\":\"Article 110292\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-09-22\",\"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/S0165168425004062\",\"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/S0165168425004062","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}
Direct positioning of multiple targets based on electromagnetic vector sensors array
Electromagnetic vector sensors (EMVSs) have gained significant attention in recent years, particularly in the field of source localization. These multi-component sensors are capable of simultaneously detecting both electric and magnetic field vector information, making them a key area of research and development. Traditional source localization methods usually estimate the source position by estimating intermediate parameters such as direction of arrival (DOA) or time of arrival (TOA) first, which involves multiple processing steps and is highly susceptible to noise. This paper employs EMVS for direct position determination (DPD), proposing distinct algorithms for line-of-sight (LOS) and multipath scenarios. In the LOS scenario, the inherent multidimensional structure of the data received by the EMVS is utilized to represent the received signal as a third-order tensor. Using the selected dual-component EMVS in this paper, data from multiple stations are concatenated into a large tensor, and the spatial location parameters of the target source are directly extracted through parallel factor (PARAFAC) decomposition. In the NLOS scenario, the data received at each station are first decorrelated, followed by direct extraction of the target source’s spatial location parameters using PARAFAC decomposition. The proposed methods eliminate the need for explicit estimation of intermediate parameters, perform localization directly in the tensor domain, and exhibit strong robustness and high capability in resolving multiple sources.
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.