Starting from 2016, the raw Global Navigation Satellite System (GNSS) measurements can be extracted from the Android Nougat (or later) operating systems. Since then, GNSS smartphone positioning has been given much attention. A high number of related publications indicates the importance of the research in this field, as it has been doing in recent years. Due to the cost-effectiveness of the GNSS smartphones, they can be employed in a wide variety of applications such as cadastral surveys, mapping surveying applications, vehicle and pedestrian navigation and etc. However, there are still some challenges regarding the noisy smartphone GNSS observations, the environment effect and smartphone holding modes and the algorithm development part which restrict the users to achieve high-precision smartphone positioning. In this review paper, we overview the research works carried out in this field with a focus on the following aspects: first, to provide a review of fundamental work on raw smartphone observations and quality assessment of GNSS observations from major smart devices including Google Pixel 4, Google Pixel 5, Xiaomi Mi 8 and Samsung Ultra S20 in terms of their signal strengths and carrier-phase continuities, second, to describe the current state of smartphone positioning research field until most recently in 2021 and, last, to summarize major challenges and opportunities in this filed. Finally, the paper is concluded with some remarks as well as future research perspectives.

Precise position information available from smartphones can play an important role in developing new location-based service (LBS) applications. Starting from 2016, and after the release of Nougat version (Version 7) by Google, developers have had access to the GNSS raw measurements through the new application programming interface (API), namely android.location (API level 24). However, the new API does not provide the typical GNSS observations directly (e.g., pseudorange, carrier-phase and Doppler observations) which have to be generated by the users themselves. Although several Apps have been developed for the GNSS observations generation, various data analyses indicate quality concerns, from biases to observation inconsistency in the generated GNSS observations output from those Apps. The quality concerns would subsequently affect GNSS data processing such as cycle slip detection, code smoothing and ultimately positioning performance. In this study, we first investigate algorithms for GNSS observations generation from the android.location API output. We then evaluate the performances of two widely used Apps (Geo++RINEX logger and GnssLogger Apps), as well as our newly developed one (namely UofC CSV2RINEX tool) which converts the CSV file to a Receiver INdependent Exchange (RINEX) file. Positioning performance analysis is also provided which indicates improved positioning accuracy using our newly developed tool. Future work finding out the potential reasons for the identified misbehavior in the generated GNSS observations is recommended; it will require a joint effort with the App developers.

In recent years, numerous smartphones have been equipped with global navigation satellite system (GNSS) technology, enabling individuals to utilize their own devices for positioning and navigation purposes. In 2016, with the launch of a mobile app by Google, namely GnssLogger, smartphone users with Android version 7 or higher were able to record raw GNSS measurements (i.e., pseudorange, carrier phase, Doppler, and carrier-to-noise density ratio (C/N0)). Since then, enhancing the accuracy and efficiency of smartphone positioning has become an interesting area of research. Precise point positioning (PPP) is a powerful method providing precise real-time positioning of a single receiver, and it can be applied to smartphone observations as well. Achieving high-precision PPP requires selecting appropriate functional and stochastic models. In this study, we investigate the development of more reliable stochastic models for smartphone GNSS observations. The least-square variance component estimation (LS-VCE) method is applied to double-difference (DD) pseudorange and carrier phase observations from two Samsung S20s to obtain appropriate variances for GPS and GLONASS. According to the results, there is no significant correlation between the pseudorange and carrier phase observations of GPS and GLONASS on the L1 frequency. Furthermore, the quality of GLONASS carrier phase observations is comparable to that of GPS. The model’s performance is then assessed with respect to single-frequency precise point positioning (SF-PPP) using a dataset collected in kinematic mode from a Samsung S20 smartphone. A significant improvement of 25.1% and 32.7% on the root-mean-square (RMS) of horizontal positioning and the 50th percentile error, respectively, was achieved when employing the obtained stochastic model.

High accuracy smartphone positioning is a research topic under extensive investigation driven by the availability of raw GNSS (GPS, Galileo, GLONASS, Beidou) carrier phase observations to Android smartphones users and great value to location-based services (van Diggelen et al, 2018; Zangenehnejad & Gao, 2021). Location-based services are to find out the geographical location of the mobile user and then provide services based on the given location information and they can touch almost every aspect of our daily life from health care, transportation, emergency and rescue services, public safety, to homeland security. High accuracy spatial location information is a vital component in developing location-based service to mobile devices, including government mandates that cellular operators must provide location of emergency callers within a certain degree of accuracy. Today, nearly every new smartphone contains a GNSS chip. While the accuracy of standard GNSS positioning with smartphones usually in the range of 5 to tens of meters, improved positioning accuracy is expected to enable many new location-based applications. This is because high accuracy will make it possible to deliver personalized services of great value to mass market applications. Better positioning accuracy, for instance, can provide new experiences in lane-level navigation and augmented reality with sensor fusion. Professional apps such as surveying could also take advantage of high accuracy positioning using much cheaper multi-constellation and multi-frequency smartphones.

Before 2016, the users had access only to the position-velocity-time (PVT) information from the GNSS chipsets, and the raw GNSS observations were not available. The GNSS module's positioning accuracy on smartphones typically ranged from 3 to 5 meters under favorable multipath conditions, but over 10 meters in challenging environments. This level of accuracy was not sufficient for some applications. Fortunately, in May 2016, during the "Google I/O" conference, Google announced that the raw GNSS measurements, i.e., the pseudorange, carrier-phase, Doppler shift and carrier-to-noise density ratio (C/N0) observations, would be accessible through the Android Nougat (version 7) operating systems. Google has officially released Android 7 (Nougat) on August 22, 2016, marking a breakthrough for the GNSS community. Since then, research has been conducted to develop new algorithms to improve GNSS positioning performance using these mass-market devices. In 2021 and 2022, the Android GPS team of Google hosted two Google smartphone decimeter challenges (GSDC), where various smartphone GNSS datasets of real vehicular applications were used to determine smartphone positioning accuracies. As has been revealed, meter-level accuracy is generally achieved by the leading participants, which is still not enough to enable smartphone precise positioning. This indicates an ongoing demand to enhance the positioning accuracy with smartphones.Different positioning algorithms, such as absolute or relative positioning methods can be applied to the smartphone observations as well. Precise point positioning (PPP) is a powerful method for conducting accurate real-time positioning using a single receiver. Research papers have reported PPP smartphone positioning accuracy ranging from decimeter to sub-meter accuracy, depending on different factors such as the environment and positioning mode (static and kinematic). Most studies have so far focused on utilizing the GNSS only observations obtained from the smartphone's API. However, incorporating additional information as constraints can enhance accuracy and overall stability (for example height information).The Android operating system incorporates a set of functions known as APIs, allowing the users to use the system's features. Each Android version has distinct types of APIs. Among these, the android.location API is dedicated to the location-related services, with the "Location" class being one of them. This class consists of parameters such as latitude, longitude, altitude, timestamp, accuracy, bearing and velocity. The "AltitudeMeters" from this class provides the height above the WGS84 ellipsoid in meters, serving as supplementary information in this research. Although the vertical positioning accuracy of GNSS is generally lower than the horizontal accuracy, utilizing recorded height from the smartphone GNSS chipset can still be beneficial. This incorporation increases the degree of freedom and strengthens the geometry of the receiver and satellites. In this study, we assess the effectiveness of the uncombined PPP model in the presence of height constraints. We will utilize both pedestrian walking and vehicular datasets collected by a dual-frequency Xiaomi Mi8 device to evaluate the effect of adding height constraint to PPP model. We expect an improvement on the root-mean-square (RMS) of horizontal positioning, the 50th percentile error, and the convergence time when employing the height constraints.