{"title":"基于线性贝叶斯滤波的低成本超宽带室内移动机器人定位系统","authors":"Shuai Zhang, Ruihua Han, Wankuan Huang, Shuaijun Wang, Qi Hao","doi":"10.1109/ICSENS.2018.8589829","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an improved UWB based indoor localization system using Bayesian filtering techniques. The system contains two key components: (1) miniaturized, high updating rate and highly reconfigurable UWB sensors with a linear regression model to calibrate range measurement errors; (2) a set of Bayesian filters which can improve the localization precision by utilizing the spatial correlation between the stationary UWB base stations and the mobile UWB station. Furthermore, a novel measurement transform is proposed to reduce the computational complexity. Experiments are performed in an indoor environment with the ground truth obtained by the motion capture system to validate and evaluate the proposed indoor localization system.","PeriodicalId":405874,"journal":{"name":"2018 IEEE SENSORS","volume":"150 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Linear Bayesian Filter Based Low-Cost UWB Systems for Indoor Mobile Robot Localization\",\"authors\":\"Shuai Zhang, Ruihua Han, Wankuan Huang, Shuaijun Wang, Qi Hao\",\"doi\":\"10.1109/ICSENS.2018.8589829\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose an improved UWB based indoor localization system using Bayesian filtering techniques. The system contains two key components: (1) miniaturized, high updating rate and highly reconfigurable UWB sensors with a linear regression model to calibrate range measurement errors; (2) a set of Bayesian filters which can improve the localization precision by utilizing the spatial correlation between the stationary UWB base stations and the mobile UWB station. Furthermore, a novel measurement transform is proposed to reduce the computational complexity. Experiments are performed in an indoor environment with the ground truth obtained by the motion capture system to validate and evaluate the proposed indoor localization system.\",\"PeriodicalId\":405874,\"journal\":{\"name\":\"2018 IEEE SENSORS\",\"volume\":\"150 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE SENSORS\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSENS.2018.8589829\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE SENSORS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSENS.2018.8589829","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Linear Bayesian Filter Based Low-Cost UWB Systems for Indoor Mobile Robot Localization
In this paper, we propose an improved UWB based indoor localization system using Bayesian filtering techniques. The system contains two key components: (1) miniaturized, high updating rate and highly reconfigurable UWB sensors with a linear regression model to calibrate range measurement errors; (2) a set of Bayesian filters which can improve the localization precision by utilizing the spatial correlation between the stationary UWB base stations and the mobile UWB station. Furthermore, a novel measurement transform is proposed to reduce the computational complexity. Experiments are performed in an indoor environment with the ground truth obtained by the motion capture system to validate and evaluate the proposed indoor localization system.