GNSS (Global Navigation Satellite System) receivers are not only able to accurately determine position, but also velocity, knowledge of which could be important in several applications. The most adopted technique for velocity estimation exploits the Doppler shift due to the relative motion between the signal source and the receiver. Alternatively, the TDCP (Time-Differenced Carrier Phase) technique, based on the differences between consecutive carrier-phase measurements, can be used. TDCP is theoretically able to achieve better performance compared with the Doppler-based approach, exploiting the high precision of a carrier-phase observable, and without suffering the ambiguity issue. The main objective of this study is to analyze TDCP performance on a smartphone GNSS chip. Smartphones GNSS receivers are usually characterized by noisy observables owing to the low quality of the antenna used; it is, therefore, interesting to compare the smartphone TDCP performance with that of the Doppler-based technique. To evaluate the benefits that TDCP can provide, especially in terms of the smartphone chip, these two approaches to velocity determination are compared using three different devices: a Novatel geodetic receiver, a u-blox multi-frequency receiver, and a Xiaomi Mi8 smartphone. The results demonstrate a performance degradation in the smartphone GNSS chip when TDCP is used, compared with the performance of higher-grade receivers. In fact, the Xiaomi Mi8 maximum errors are greater than those of the Novatel geodetic receiver, but they are still acceptable as they do not exceed 6 cm/s, making the TDCP technique a valid approach for advanced algorithms; indeed, TDCP velocity demonstrates a few mm/s accuracy with a smartphone. The application of a RAIM algorithm enables error reduction and the achievement of reliable information; the obtained solution reliability is about 89%.

Employment of precise positioning techniques will enable low-cost receivers for a variety of applications. The complexity of techniques such as Precise Point Positioning (PPP), or differential techniques that require the use of external sources of corrections, could be a disadvantage for users. On the other hand, a simple technique such as Single-Point Positioning (SPP) alone does not provide high-level accuracy. Nevertheless, the entry Galileo High-Accuracy Service (HAS), even if developed to be applied on PPP, could offer a positive impact on SPP. The objective of this study is the analysis of the effects of HAS on SPP, which are evaluated for Galileo and GPS, in single- and double-constellation mode. Results are encouraging, especially on the vertical channel, where some centimetric improvements are obtained.

The objective of this work is the evaluation of the performances of EGNOS (European Geostationary Navigation Overlay System) augmentation system in maritime navigation by comparing them with those obtained by other positioning methods as Single Point Positioning (SPP) and Differential Global Positioning System (DGPS). Preliminarily, EGNOS performances in an open-sky context were evaluated through static data downloaded by EGNOS RIMS (Ranging and Integrity Monitoring Stations) located in Rome. Then, for the maritime test carried out onboard a boat in the Gulf of Naples, two dual-frequency receivers were used: Xiaomi Mi 8 smartphone and u-blox ZED-F9P multi-band GNSS (Global Navigation Satellite System) receiver, both in kinematic mode. At last, IMO (International Maritime Organization) requirements, established in IMO Resolution A.1046 (27), that a SBAS (Satellite Based Augmentation System) system in particular scenarios (coastal, inland-water, harbor navigation and ocean waters) must respect, were verified.

Velocity estimation has a key role in several applications; for instance, velocity estimation in navigation or in mobile mapping systems and GNSSs is currently a common way to achieve reliable and accurate velocity. Two approaches are mainly used to obtain velocity based on GNSS measurements, i.e., Doppler observations and carrier phases differenced in time (that is, TDCP). In a benign environment, Doppler-based velocity can be estimated accurately to within a few cm/s, while TDCP-based velocity can be estimated accurately to within a few mm/s. On the other hand, the TDCP technique is more prone to availability shortage and the presence of blunders. In this work, the two mentioned approaches are tested, using three devices of different grades: a high-grade geodetic receiver, a high-sensitivity receiver, and a GNSS chip mounted on a smartphone. The measurements of geodetic receivers are inherently cleaner, providing an accurate solution, while the remaining two receivers provide worse results. The case of smartphone GNSS chips can be particularly critical owing to the equipped antenna, which makes the measurements noisy and largely affected by blunders. The GNSSs are considered separately in order to assess the performance of the single systems. The analysis carried out in this research confirms the previous considerations about receiver grades and processing techniques. Additionally, the obtained results highlight the necessity of adopting a diagnostic approach to the measurements, such as RAIM-FDE, especially for low-grade receivers.

Since 2016, it has been possible to record and collect GNSS (Global Navigation Satellite System) raw data on Android devices. This has been a game changer in low-cost device applications. Researchers in the GNSS field have tried to answer new questions regarding Android device positioning performance, the quality of their measurements, positioning techniques that could be applied, and methods to increase the accuracy and the reliability of the PNT (Position Navigation Timing) solution. Several research groups have demonstrated accurate positioning using smartphones while also showing the potential limitations of such devices. In particular, problems related to the GNSS antenna performance have been reported; indeed, in urban scenarios, where users typically operate, the presence of multiple outliers could make the navigation solution inaccurate, if not unfeasible. Hence, techniques useful for verifying the reliability of the navigation solutions have become fundamental. The reliability of the PNT solution provided by smartphones is an open research question. In this study, traditional RAIM (Receiver Autonomous Integrity Monitoring) algorithms were adapted to the case of smartphones. Navigation solution algorithms, including FDE (Fault Detection and Exclusion), were tested using a long data collection made by a smartphone located in a harsh environment in static mode. The performance of the proposed approaches was assessed in terms of horizontal and vertical errors, solution reliability, and residual distribution.

GNSS navigation can be challenging in urban environments, especially when low-cost devices are adopted. Among the possible solutions, in more recent years, approaches based on Machine Learning became popular. In this work, features based on geometry, satellite visibility and carrier-to-noise ratio are used in combination with k-Nearest Neighbors classifier to distinguish between open-sky and obstructed environments. The purpose of this research is to develop a reliable context classifier, to evaluate its recognition capabilities in static and dynamic environments and to assess its applicability in real-time positioning. Several performance metrics have been used, i.e., accuracy, precision, recall, F1-score, and multiple tests have been carried out to demonstrate the reliability of such algorithm with validation data. More than 98% of classification accuracy for the static tests has been obtained in average, evidencing the detection capabilities of such an algorithm.