{"title":"Mobile location tracking with NLOS error mitigation","authors":"C. Wann, Yi-Ming Chen, Ming-Shiung Lee","doi":"10.1109/GLOCOM.2002.1188485","DOIUrl":null,"url":null,"abstract":"We present a mobile location tracking method with non-line-of-sight (NLOS) error mitigation for network-based wireless location systems. Based on the reconstructed LOS time of arrival (TOA) results from NLOS propagation identification and correction, geometric location techniques are used to calculate the location estimate of a mobile station. General TOA geometric cases and positioning solutions are analyzed. The time sequence of location samples is processed by Kalman filtering for position tracking and velocity estimation. A post-Kalman filter smoothing method is also applied to improve the velocity estimation. It is shown that the location tracking error can be reduced by processing the reconstructed line-of-sight TOA measurements with Kalman filtering.","PeriodicalId":415837,"journal":{"name":"Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GLOCOM.2002.1188485","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22
Abstract
We present a mobile location tracking method with non-line-of-sight (NLOS) error mitigation for network-based wireless location systems. Based on the reconstructed LOS time of arrival (TOA) results from NLOS propagation identification and correction, geometric location techniques are used to calculate the location estimate of a mobile station. General TOA geometric cases and positioning solutions are analyzed. The time sequence of location samples is processed by Kalman filtering for position tracking and velocity estimation. A post-Kalman filter smoothing method is also applied to improve the velocity estimation. It is shown that the location tracking error can be reduced by processing the reconstructed line-of-sight TOA measurements with Kalman filtering.