Abstract
In an urban scenario, GNSS performance is strongly influenced by gross errors in the measurements, usually related to multipath and non-line-of-sight phenomena. The use of RAIM algorithms is a common approach to solve this issue. A significant amount of the existing GNSS receivers is currently mounted on smart devices, above all, smartphones. A typical drawback of these devices is the unavailability of raw measurements, which does not allow fully exploiting the GNSS potential; in particular, this feature limits the use of RAIM algorithms. Since 2016, for few smart devices, it has been finally possible to access GNSS raw measurements, allowing the implementation of specific algorithms and enabling new services. The Xiaomi Mi 8 is equipped with the Broadcom BCM47755 receiver, able to provide dual-frequency raw measurements from quad-constellation GPS, Glonass, Galileo, BeiDou. In this work, the performance in an urban area of the Xiaomi Mi8 GNSS was analyzed. An important issue of smartphone GNSS is related to the antenna, which is not able to protect from the multipath phenomenon; this issue has a large probability to emerge in hostile environments like urban areas. As a term of comparison, the high-sensitivity receiver NVS NV08C-CSM, connected to a patch antenna, was used. In particular, the considered receivers were placed in the same location, and their positions were estimated in single point positioning, applying a classical RAIM algorithm. An error analysis was carried out, and the obtained results demonstrated the effectiveness of RAIM when applied to Xiaomi Mi8 GNSS measurements.
Highlights
A large amount of GNSS receivers is installed into smart devices
The considered receivers were placed in the same location, and their positions were estimated in single point positioning, applying a classical RAIM algorithm
The performance of a particular device, Xiaomi Mi8, was assessed in an urban environment, applying the RAIM technique to mitigate the effect of the multipath phenomenon
Summary
A large amount of GNSS receivers is installed into smart devices. In 2020, the number of GNSS devices in use is almost 7 billion; about 6 billion are in the consumer solution market, and about 3.5 billion of these are into smartphones. A typical drawback of the smartphone GNSS is the unavailability of raw measurements, which does not allow fully exploiting the device capability. In May 2016, Google announced the possibility to retrieve GNSS raw measurements from Android 7 smart devices. This technical development has opened the door to a wide range of possible applications for smartphones, based on advanced GNSS processing techniques, typically restricted to professional receivers. The performance assessment of RAIM, applied to the smartphone, is the main scope of this work
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