{"title":"基于蓝牙的最低基础设施家庭定位系统","authors":"Damian Kelly, S. McLoone, T. Dishongh","doi":"10.1109/ISWCS.2008.4726134","DOIUrl":null,"url":null,"abstract":"Indoor location tracking is a function best suited to wireless LAN devices. This generally precludes it from home use in isolated rural areas, where WLAN is a rare commodity. We propose an affordable localisation system which can be implemented using a variety of Bluetooth enabled mobile phones. This permits the incorporation of cellular network signal measurements as well as Bluetooth link measurements into the localisation framework. This paper presents a Hidden Markov Model localisation method, utilising the Viterbi algorithm, which enables single Bluetooth access point localisation. The improvement of accuracy this presents over a Naive Bayes classifier is illustrated, along with the optimal method of obtaining training data.","PeriodicalId":158650,"journal":{"name":"2008 IEEE International Symposium on Wireless Communication Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"A Bluetooth-based minimum infrastructure home localisation system\",\"authors\":\"Damian Kelly, S. McLoone, T. Dishongh\",\"doi\":\"10.1109/ISWCS.2008.4726134\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Indoor location tracking is a function best suited to wireless LAN devices. This generally precludes it from home use in isolated rural areas, where WLAN is a rare commodity. We propose an affordable localisation system which can be implemented using a variety of Bluetooth enabled mobile phones. This permits the incorporation of cellular network signal measurements as well as Bluetooth link measurements into the localisation framework. This paper presents a Hidden Markov Model localisation method, utilising the Viterbi algorithm, which enables single Bluetooth access point localisation. The improvement of accuracy this presents over a Naive Bayes classifier is illustrated, along with the optimal method of obtaining training data.\",\"PeriodicalId\":158650,\"journal\":{\"name\":\"2008 IEEE International Symposium on Wireless Communication Systems\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Symposium on Wireless Communication Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISWCS.2008.4726134\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Symposium on Wireless Communication Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISWCS.2008.4726134","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Bluetooth-based minimum infrastructure home localisation system
Indoor location tracking is a function best suited to wireless LAN devices. This generally precludes it from home use in isolated rural areas, where WLAN is a rare commodity. We propose an affordable localisation system which can be implemented using a variety of Bluetooth enabled mobile phones. This permits the incorporation of cellular network signal measurements as well as Bluetooth link measurements into the localisation framework. This paper presents a Hidden Markov Model localisation method, utilising the Viterbi algorithm, which enables single Bluetooth access point localisation. The improvement of accuracy this presents over a Naive Bayes classifier is illustrated, along with the optimal method of obtaining training data.