Shixun Wu;Miao Zhang;Kanapathippillai Cumanan;Kai Xu;Zhangli Lan
{"title":"Single base station tracking approaches with hybrid TOA/AOD/AOA measurements in different propagation environments","authors":"Shixun Wu;Miao Zhang;Kanapathippillai Cumanan;Kai Xu;Zhangli Lan","doi":"10.23919/JCN.2024.000053","DOIUrl":null,"url":null,"abstract":"In this paper, mobile terminal (MT) tracking based on time of arrival (TOA), angle of departure (AOD), and angle of arrival (AOA) measurements with one base station is investigated. The main challenge is the unknown propagation environment, such as line-of-sight (LOS), non-line-of-sight (NLOS) modeled as one-bounce scattering or mixed LOS/NLOS propagations, which may result in heterogeneous measurements. For LOS scenario, a linear Kalman filter (LKF) algorithm is adopted through analyzing and deriving the estimated error of MT. For NLOS scenario, as the position of scatterer is unknown, a nonlinear range equation is formulated to measure the actual AOD/AOA measurements and the position of scatterer, and three different algorithms: The extended Kalman filter (EKF), unscented Kalman filter (UKF) and an approximated LKF are developed. For mixed LOS/NLOS scenario, the modified interacting multiple model LKF (M-IMM-LKF) and the identified LKF algorithms (I-LKF) are utilized to address the issue of the frequent transition between LOS and NLOS propagations. In comparison with EKF and UKF algorithms, the simulation results and running time comparisons show the superiority and effectiveness of the LKF algorithm in LOS and NLOS scenarios. Both M-IMM-LKF and I-LKF algorithms are capable to significantly reduce the localization errors, and better than three existing algorithms.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"26 6","pages":"617-631"},"PeriodicalIF":2.9000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834482","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Communications and Networks","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10834482/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, mobile terminal (MT) tracking based on time of arrival (TOA), angle of departure (AOD), and angle of arrival (AOA) measurements with one base station is investigated. The main challenge is the unknown propagation environment, such as line-of-sight (LOS), non-line-of-sight (NLOS) modeled as one-bounce scattering or mixed LOS/NLOS propagations, which may result in heterogeneous measurements. For LOS scenario, a linear Kalman filter (LKF) algorithm is adopted through analyzing and deriving the estimated error of MT. For NLOS scenario, as the position of scatterer is unknown, a nonlinear range equation is formulated to measure the actual AOD/AOA measurements and the position of scatterer, and three different algorithms: The extended Kalman filter (EKF), unscented Kalman filter (UKF) and an approximated LKF are developed. For mixed LOS/NLOS scenario, the modified interacting multiple model LKF (M-IMM-LKF) and the identified LKF algorithms (I-LKF) are utilized to address the issue of the frequent transition between LOS and NLOS propagations. In comparison with EKF and UKF algorithms, the simulation results and running time comparisons show the superiority and effectiveness of the LKF algorithm in LOS and NLOS scenarios. Both M-IMM-LKF and I-LKF algorithms are capable to significantly reduce the localization errors, and better than three existing algorithms.
期刊介绍:
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