# Real-Time Geophysical Applications with Android GNSS Raw Measurements
## 1 Introduction  
The technological innovations introduced by Google in smartphone and internet of things (IoT) devices during the past two years started a revolution within the mass-market GNSS world. The release of the Android Raw GNSS Measurements API (Figure 1), compatible with Android 7.0 or higher, allows app developers to access not only the internal position-velocity-time (PVT) solution, but also the raw GNSS measurements. This enables the reconstruction of pseudorange, carrier-phase, Doppler, and signal-to-noise ratio (SNR) observables. The number of Android smartphones compatible with the Google API is increasing day-by-day. During the first months of 2017, the Samsung S8 and Huawei P10 were released as the first multi-GNSS smartphones able to track carrier-phase measurements. However, the real turning point was the release of the Xiaomi Mi 8 in May 2018. This device is the first multi-GNSS dual-frequency (L1/E1 and L5/E5a) smartphone using the new Broadcom BCM47755 GNSS chipset. In fact, the availability of L5/E5a measurements ensures improvements in signal tracking and positioning performances when combined with the traditional L1/E1. Furthermore, the release of Android 9, which allows the acquisition of carrier-phase measurements not affected by duty cycle--a battery-saving mode leadingto continuous cycle slip in carrier-phase measurements--indicates the leading position that Android is assuming within the mass-market GNSS world. The availability of GNSS measurements from such mass-market devices encourages researchers to extend the use of traditional algorithms that were originally developed for high-end receivers to include this new generation of GNSS devices, aiming mainly at increasing the positioning accuracy. Real-time kinematic (RTK) and Precise Point Positioning (PPP), for example, are widely used to exploit the benefits coming from the accessibility of the raw GNSS measurements [1; 2; 3; 4]. However, high accuracy is prevented by the quality of GNSS measurements obtainable from Android smartphones, due especially to the characteristics of the GNSS antenna: shared with other communication systems (WiFi, Bluetooth, etc. ), low cost, and linearly polarized [5; 6]. The research carried out so far has highlighted the low resistance to interference, cycle slip, and multipath effects, as well as a marked gain in the SNR with respect the geodetic class receivers [7]. Researchers are also focusing their attention on the development of new positioning algorithms based on the integration of different GNSS measurements such as pseudorange and doppler and/or carrier-phase [5; 8], or on the combination of GNSS and external information to overcome the low accuracy achievable with GNSS observables from Android devices. Despite the problems affecting these GNSS measurements--especially in urban environments--smartphone position, especially for the real-time navigation of pedestrians and vehicles, is the first beneficiary of the new possibility offered by Google. However, the availability of multi-GNSS and multi-frequency smartphones also opens possibilities to new applications not immediately related to positioning. Innovative research fields are inspired by the GNSS community through the European GNSS Agency (GSA) and its GNSS Raw Measurements Task Force, launched in 2017, which now brings together around 100 members from international agencies, universities, research institutes, and companies. Following the main research fields in GNSS, this work aims at investigating the real-time performances of Android smartphones in the description and reconstruction of phenomena in the geosciences field. The Geodesy and Geomatics Division of Sapienza University of Rome is responsible for the development of different GNSS algorithms to enhance the integration of GNSS within geophysical applications. VADASE (Variometric Approach for Displacements Analysis Stand-alone Engine) and VARION (Variometric Approach for Real-time Ionospheric Observations) are two  
Figure 1: From Location API 23 to Location API 24.