{"title":"A Two-step Constrained Least Squares Localization in Wireless Sensor Networks","authors":"Guangzhe Liu, Jingyu Hua, Feng Li, Yu Zhang, Zhijiang Xu","doi":"10.1109/ICCCAS.2018.8768919","DOIUrl":null,"url":null,"abstract":"In wireless positioning, the time delay due to non-line-of-sight propagation will significantly reduce the localization accuracy of traditional algorithms. Therefore, this paper proposes a two-step constraint least squares (CLS) algorithm, in which a new range constrain is constructed with the help of coarse positioning estimation, while the coarse estimation is produced by the conventional CLS scheme. Consequently, the quadratic programming scheme is operated to enhance the final localization performance. Simulations demonstrate that the proposed algorithm greatly enhances the localization accuracy in the NLOS environment, and outperforms the tested opponents. Moreover, we also observe a stable performance of the proposed algorithm in terms of the measurement noise variations.","PeriodicalId":166878,"journal":{"name":"2018 10th International Conference on Communications, Circuits and Systems (ICCCAS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 10th International Conference on Communications, Circuits and Systems (ICCCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCAS.2018.8768919","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In wireless positioning, the time delay due to non-line-of-sight propagation will significantly reduce the localization accuracy of traditional algorithms. Therefore, this paper proposes a two-step constraint least squares (CLS) algorithm, in which a new range constrain is constructed with the help of coarse positioning estimation, while the coarse estimation is produced by the conventional CLS scheme. Consequently, the quadratic programming scheme is operated to enhance the final localization performance. Simulations demonstrate that the proposed algorithm greatly enhances the localization accuracy in the NLOS environment, and outperforms the tested opponents. Moreover, we also observe a stable performance of the proposed algorithm in terms of the measurement noise variations.