Android GNSS数据的统计研究——以小米Mi 8双频原始测量为例

L. Massarweh, F. Darugna, D. Psychas, Jon Bruno
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引用次数: 10

摘要

安卓7.0于2016年8月正式发布,是全球导航卫星系统(GNSS)社区的一个突破。从引入其应用程序编程接口24 (API 24)开始,Android用户已经能够在他们的智能手机中直接访问原始的GNSS测量,独立于特定的手机设计或制造商。在本研究中,我们将小米Mi 8作为案例研究设备,在升级到Android 9.0之后,配备了BCM47755位置集线器(第一个双频GNSS芯片组),以访问API 28中实现的新功能。在基于智能手机的定位中,对原始测量值及其质量的分析是至关重要的,因为对伪距测量值的偏差或极其嘈杂的估计将妨碍精确的定位性能。在这里,我们报告了在静态模式下在大地测量柱上进行的几次测试,在类似的条件下,覆盖的时间跨度长达几个小时。首先描述从API检索到的主要数量,然后根据从所有这些长数据集检索到的统计信息进行特征化。经过一些考虑,我们研究了其中一些变量的载波噪声密度比(C/N0)的依赖性,以及API多路径指标对可用观测值的影响。最后,为了支持基于智能手机的精确定位,本文提出了基于C/ no的数据“数量”和“质量”之间的初步权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical Investigation of Android GNSS Data: Case Study Using Xiaomi Mi 8 Dual-Frequency Raw Measurements
Officially released on August 2016, the Android 7.0 has been a breakthrough for the Global Navigation Satellite System (GNSS) community. Starting with the introduction of its Application Programming Interface 24 (API 24), Android users have been able to directly access raw GNSS measurements within their smartphone, independently on the specific phone design or manufacturer. In this research we consider as a case-study device the Xiaomi Mi 8, equipped with the BCM47755 location hub (first dual-frequency GNSS chipset), after being upgrade to Android 9.0 in order to access new functionalities implemented in the API 28. The analysis of raw measurements and their quality is fundamental in smartphone-based positioning, since biased or extremely noisy estimates of the pseudorange measurements will hamper the precise positioning performances. Here, we report on several tests performed in static mode over a geodetic pillar, under similar conditions and covering time-spans up to several hours. Main quantities retrieved from the API were first described and then characterized based on the statistics retrieved from all these long dataset. After some considerations, a dependency w.r.t. the carrier-to-noise density ratio (C/N0) has been investigated for some of these variables, as well as for the impact of the API multipath indicator on the available observations. Finally, a preliminary C/N0-based trade-off between data ‘quantity’ and ‘quality’ is here proposed in order to support smartphone-based precise positioning.
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