{"title":"基于wifi的室内定位算法评价","authors":"Ahed Aboodi, T. Wan","doi":"10.1109/MUSIC.2012.52","DOIUrl":null,"url":null,"abstract":"This paper proposes an indoor positioning algorithm, WBI based on WiFi Received Signal Strength (RSS) technology in conjunction with trilateration techniques. The WBI algorithm estimates the location using RSS values previously collected from within the area of interest, determine whether it falls within the Min-Max bounding box, corrects for non-line-of-sight propagation effects on positioning errors using Kalman filtering, and finally update the location estimation using Least Square Estimation (LSE). The paper analyzes the complexity of the proposed algorithm and compares its performance against existing algorithms. Furthermore, the proposed WBI algorithm was able to achieve an average accuracy of 2.6 m.","PeriodicalId":260515,"journal":{"name":"2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Evaluation of WiFi-Based Indoor (WBI) Positioning Algorithm\",\"authors\":\"Ahed Aboodi, T. Wan\",\"doi\":\"10.1109/MUSIC.2012.52\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes an indoor positioning algorithm, WBI based on WiFi Received Signal Strength (RSS) technology in conjunction with trilateration techniques. The WBI algorithm estimates the location using RSS values previously collected from within the area of interest, determine whether it falls within the Min-Max bounding box, corrects for non-line-of-sight propagation effects on positioning errors using Kalman filtering, and finally update the location estimation using Least Square Estimation (LSE). The paper analyzes the complexity of the proposed algorithm and compares its performance against existing algorithms. Furthermore, the proposed WBI algorithm was able to achieve an average accuracy of 2.6 m.\",\"PeriodicalId\":260515,\"journal\":{\"name\":\"2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MUSIC.2012.52\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MUSIC.2012.52","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evaluation of WiFi-Based Indoor (WBI) Positioning Algorithm
This paper proposes an indoor positioning algorithm, WBI based on WiFi Received Signal Strength (RSS) technology in conjunction with trilateration techniques. The WBI algorithm estimates the location using RSS values previously collected from within the area of interest, determine whether it falls within the Min-Max bounding box, corrects for non-line-of-sight propagation effects on positioning errors using Kalman filtering, and finally update the location estimation using Least Square Estimation (LSE). The paper analyzes the complexity of the proposed algorithm and compares its performance against existing algorithms. Furthermore, the proposed WBI algorithm was able to achieve an average accuracy of 2.6 m.