{"title":"利用FLIP实现鲁棒无线电定位","authors":"Reinhard Müllner, Thomas Burgess","doi":"10.1109/EURONAV.2018.8433241","DOIUrl":null,"url":null,"abstract":"Radio fingerprinting based localization relies on comparing observations to a database of reference radio fingerprint point data. For complex buildings these databases can be very large. As mobile devices have limitations in storage capacity, working memory, processing speed, and power usage, making an on-terminal system that works well even in large installations is challenging. Moreover, the reported Received Signal Strength Indication (RSSI) scale often differ between devices, so that naive approaches for fingerprint similarity easily can fail to produce reliable results. In this paper, FLexible Indoor Position (FLIP) is presented to address these issues. It provides efficient device independent positioning even in complex buildings, while also taking inhomogeneous transmitter power levels and radio map irregularities into consideration. Despite plain accuracy not being the main goal of FLIP, when it was evaluated on the raw UJIIndoorLoc WiFi database it yielded a median positioning error of 4.7 m (and above 93 % floor level/building identification success rate), which is competitive to other significantly more computation intense approaches. In commercial applications with dedicated iBeacon infrastructures, FLIP routinely reaches median errors below 2 m.","PeriodicalId":434266,"journal":{"name":"2018 European Navigation Conference (ENC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust radio localization with FLIP\",\"authors\":\"Reinhard Müllner, Thomas Burgess\",\"doi\":\"10.1109/EURONAV.2018.8433241\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Radio fingerprinting based localization relies on comparing observations to a database of reference radio fingerprint point data. For complex buildings these databases can be very large. As mobile devices have limitations in storage capacity, working memory, processing speed, and power usage, making an on-terminal system that works well even in large installations is challenging. Moreover, the reported Received Signal Strength Indication (RSSI) scale often differ between devices, so that naive approaches for fingerprint similarity easily can fail to produce reliable results. In this paper, FLexible Indoor Position (FLIP) is presented to address these issues. It provides efficient device independent positioning even in complex buildings, while also taking inhomogeneous transmitter power levels and radio map irregularities into consideration. Despite plain accuracy not being the main goal of FLIP, when it was evaluated on the raw UJIIndoorLoc WiFi database it yielded a median positioning error of 4.7 m (and above 93 % floor level/building identification success rate), which is competitive to other significantly more computation intense approaches. In commercial applications with dedicated iBeacon infrastructures, FLIP routinely reaches median errors below 2 m.\",\"PeriodicalId\":434266,\"journal\":{\"name\":\"2018 European Navigation Conference (ENC)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 European Navigation Conference (ENC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EURONAV.2018.8433241\",\"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 European Navigation Conference (ENC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EURONAV.2018.8433241","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
基于无线电指纹的定位依赖于将观测值与参考无线电指纹点数据数据库进行比较。对于复杂的建筑物,这些数据库可能非常大。由于移动设备在存储容量、工作内存、处理速度和功耗方面存在限制,因此制作一个即使在大型安装中也能正常工作的终端系统是一项挑战。此外,报告的接收信号强度指示(Received Signal Strength Indication, RSSI)尺度在不同设备之间往往不同,因此简单的指纹相似性方法很容易无法产生可靠的结果。为了解决这些问题,本文提出了柔性室内体位(FLIP)。即使在复杂的建筑物中,它也能提供有效的设备独立定位,同时还考虑了不均匀的发射机功率水平和无线电地图的不规则性。尽管简单的准确性不是FLIP的主要目标,但当它在原始UJIIndoorLoc WiFi数据库上进行评估时,它产生的中位定位误差为4.7 m(高于93%的楼层/建筑物识别成功率),这与其他计算强度更高的方法相比具有竞争力。在具有专用iBeacon基础设施的商业应用中,FLIP的中值误差通常低于2米。
Radio fingerprinting based localization relies on comparing observations to a database of reference radio fingerprint point data. For complex buildings these databases can be very large. As mobile devices have limitations in storage capacity, working memory, processing speed, and power usage, making an on-terminal system that works well even in large installations is challenging. Moreover, the reported Received Signal Strength Indication (RSSI) scale often differ between devices, so that naive approaches for fingerprint similarity easily can fail to produce reliable results. In this paper, FLexible Indoor Position (FLIP) is presented to address these issues. It provides efficient device independent positioning even in complex buildings, while also taking inhomogeneous transmitter power levels and radio map irregularities into consideration. Despite plain accuracy not being the main goal of FLIP, when it was evaluated on the raw UJIIndoorLoc WiFi database it yielded a median positioning error of 4.7 m (and above 93 % floor level/building identification success rate), which is competitive to other significantly more computation intense approaches. In commercial applications with dedicated iBeacon infrastructures, FLIP routinely reaches median errors below 2 m.