{"title":"非视距条件下移动站定位的Kriging估计","authors":"J.Y. Huang, Z.X. Chen, Q. Wan, W.L. Yang","doi":"10.1109/ICCCAS.2007.6250846","DOIUrl":null,"url":null,"abstract":"Estimation of mobile station location is made difficult by nonsymmetric contamination of measured data caused by NLOS propagation effects. In this paper, a novel algorithm based on kriging method is proposed to reduce the effects of NLOS propagation on the location error. Simulation results show that the proposed algorithm clearly outperforms two other algorithms (non-parametric kernel method and parametric least square method) and comes close to meeting Cramer Rao lower bound (CRLB).","PeriodicalId":218351,"journal":{"name":"2007 International Conference on Communications, Circuits and Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Kriging estimator for mobile station positioning under non-line-of-sight (NLOS) conditions\",\"authors\":\"J.Y. Huang, Z.X. Chen, Q. Wan, W.L. Yang\",\"doi\":\"10.1109/ICCCAS.2007.6250846\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Estimation of mobile station location is made difficult by nonsymmetric contamination of measured data caused by NLOS propagation effects. In this paper, a novel algorithm based on kriging method is proposed to reduce the effects of NLOS propagation on the location error. Simulation results show that the proposed algorithm clearly outperforms two other algorithms (non-parametric kernel method and parametric least square method) and comes close to meeting Cramer Rao lower bound (CRLB).\",\"PeriodicalId\":218351,\"journal\":{\"name\":\"2007 International Conference on Communications, Circuits and Systems\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 International Conference on Communications, Circuits and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCAS.2007.6250846\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Conference on Communications, Circuits and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCAS.2007.6250846","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Kriging estimator for mobile station positioning under non-line-of-sight (NLOS) conditions
Estimation of mobile station location is made difficult by nonsymmetric contamination of measured data caused by NLOS propagation effects. In this paper, a novel algorithm based on kriging method is proposed to reduce the effects of NLOS propagation on the location error. Simulation results show that the proposed algorithm clearly outperforms two other algorithms (non-parametric kernel method and parametric least square method) and comes close to meeting Cramer Rao lower bound (CRLB).
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
