{"title":"使用Android GNSS原始测量来估计实时变速的大众市场应用的新机会","authors":"Marco Fortunato, A. Mazzoni","doi":"10.23919/ENC48637.2020.9317397","DOIUrl":null,"url":null,"abstract":"Few years after the first release of Android GNSS Raw Measurements API, Android smartphones are increasingly becoming the most competitive GNSS mass-market device. The developments in GNSS space and user - mainly related to smartphone manufacturers - segments allowed to show meter and sub-meter accuracy in static and kinematic applications using GNSS observations collected from Android smartphones. Unlike the large number of published researches which deals with the analyses of Android GNSS Raw Measurements and the achievable accuracy in the position domain, the aim of this work is to study the velocity field directly estimated in pedestrian scenarios from Android GNSS measurements. The 3D velocity, estimated with accuracy from few mm/s to 1–2 cm/s - respectively for the horizontal and vertical components - with the kin-VADASE (Variometric Apporach for Displacement Analysis Stand-alone Engine) developed at Sapienza University of Rome, are here applied in the field of gestures reconstruction and heading determination in pedestrian scenarios. The results discussed in the paper show immediate, stable and reliable velocity confirming the key role that Android smartphones are acquiring in mass-market application, e.g. mHealth, AR, fitness and sports.","PeriodicalId":157951,"journal":{"name":"2020 European Navigation Conference (ENC)","volume":"272 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"New Opportunities for Mass-Market Applications of Real-Time Variometric Velocity Estimated Using Android GNSS Raw Measurements\",\"authors\":\"Marco Fortunato, A. Mazzoni\",\"doi\":\"10.23919/ENC48637.2020.9317397\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Few years after the first release of Android GNSS Raw Measurements API, Android smartphones are increasingly becoming the most competitive GNSS mass-market device. The developments in GNSS space and user - mainly related to smartphone manufacturers - segments allowed to show meter and sub-meter accuracy in static and kinematic applications using GNSS observations collected from Android smartphones. Unlike the large number of published researches which deals with the analyses of Android GNSS Raw Measurements and the achievable accuracy in the position domain, the aim of this work is to study the velocity field directly estimated in pedestrian scenarios from Android GNSS measurements. The 3D velocity, estimated with accuracy from few mm/s to 1–2 cm/s - respectively for the horizontal and vertical components - with the kin-VADASE (Variometric Apporach for Displacement Analysis Stand-alone Engine) developed at Sapienza University of Rome, are here applied in the field of gestures reconstruction and heading determination in pedestrian scenarios. The results discussed in the paper show immediate, stable and reliable velocity confirming the key role that Android smartphones are acquiring in mass-market application, e.g. mHealth, AR, fitness and sports.\",\"PeriodicalId\":157951,\"journal\":{\"name\":\"2020 European Navigation Conference (ENC)\",\"volume\":\"272 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 European Navigation Conference (ENC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/ENC48637.2020.9317397\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 European Navigation Conference (ENC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ENC48637.2020.9317397","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
在Android GNSS Raw Measurements API首次发布几年后,Android智能手机正日益成为最具竞争力的GNSS大众市场设备。GNSS空间和用户(主要与智能手机制造商有关)细分市场的发展允许使用从Android智能手机收集的GNSS观测数据在静态和运动学应用中显示米级和亚米级精度。与大量已发表的关于Android GNSS原始测量数据分析和位置域可实现精度的研究不同,本研究的目的是研究Android GNSS测量数据在行人场景下直接估计的速度场。利用罗马萨皮恩扎大学开发的kin-VADASE(位移分析独立引擎的可变方法),3D速度的估计精度从几毫米/秒到1-2厘米/秒,分别适用于水平和垂直组件,应用于行人场景中的手势重建和方向确定领域。论文中讨论的结果显示了即时,稳定和可靠的速度,证实了Android智能手机在大众市场应用中的关键作用,例如移动健康,增强现实,健身和运动。
New Opportunities for Mass-Market Applications of Real-Time Variometric Velocity Estimated Using Android GNSS Raw Measurements
Few years after the first release of Android GNSS Raw Measurements API, Android smartphones are increasingly becoming the most competitive GNSS mass-market device. The developments in GNSS space and user - mainly related to smartphone manufacturers - segments allowed to show meter and sub-meter accuracy in static and kinematic applications using GNSS observations collected from Android smartphones. Unlike the large number of published researches which deals with the analyses of Android GNSS Raw Measurements and the achievable accuracy in the position domain, the aim of this work is to study the velocity field directly estimated in pedestrian scenarios from Android GNSS measurements. The 3D velocity, estimated with accuracy from few mm/s to 1–2 cm/s - respectively for the horizontal and vertical components - with the kin-VADASE (Variometric Apporach for Displacement Analysis Stand-alone Engine) developed at Sapienza University of Rome, are here applied in the field of gestures reconstruction and heading determination in pedestrian scenarios. The results discussed in the paper show immediate, stable and reliable velocity confirming the key role that Android smartphones are acquiring in mass-market application, e.g. mHealth, AR, fitness and sports.