Snapshot Estimation Algorithms for GNSS Mass-Market Receivers

This thesis resumes the PhD program carried out in the signal processing, satellite positioning and telecommunication fields, within the Navigation, Signal Analysis and Simulation (NavSAS) group, Department of Electronics and Telecommunications (DET) of Politecnico di Torino, in the period going from January 2012 to December 2014. The main topic of the PhD activity is represented by Global Navigation Satellite System (GNSS) receivers core technologies. In particular, it deals with the design, development, test and performance assessment of innovative architectures, techniques, and algorithms for Global Positioning System (GPS) and Galileo receivers, both professional high performance and commercial mass-market. GPS, and in general GNSSs are radio-communication infrastructures, aimed to enable a generic user to compute Position, Velocity and Time (PVT). The signals transmitted by a constellation of satellites are processed by an electronic device, performing trilateration with respect to the satellites, taken as reference points. At least 4 satellites are required to be in Line of Sight (LOS) with the receiver, so as to obtain 4 different signals and to solve the 4 navigation unknowns: latitude, longitude, height and time. Since their first appearance, in the early seventies, GNSS chipsets and devices are gaining a fundamental role in most applications of everyday life, and their global market continues to grow rapidly. In 25 years, GNSS receivers became extremely used worldwide, not only for positioning and navigation purposes, but also for time synchronization, thus spanning an unlimited range of applications, from commercial to scientific, from military to recreational. GNSS mass-market receivers are extremely widespread, produced in very high volume—hundreds of millions just for smartphones and tablets—and sold at a limited price. This variety of applications and possibilities represents the main reason of the continuous growth of the GNSS field: in fact, new systems are emerging beside GPS, such as GLONASS, currently operational and in expansion, Galileo and Beidou. With the latest trends of multi-constellation receivers, the positioning accuracy can greatly improve, as well as its robustness, availability, reliability, but at the expense of a greater complexity and power consumption

Software-defined radio technology for GNSS scintillation analysis: bring Antarctica to the lab

Global navigation satellite systems (GNSSs) are widely used to support logistics, scientific operations, and to monitor the polar ionosphere indirectly, which is a region characterized by strong phase scintillation events that severely affect the quality and reliability of received signals. Professional commercial GNSS receivers are widely used for scintillation monitoring; on the contrary, custom-designed solutions based on data grabbers and software receivers constitute novelty. The latter enables a higher level of flexibility and configurability, which is important when working in remote and severe environments. We describe the scientific, technological, and logistical challenges of installing an ionospheric monitoring station in Antarctica, based on a multi-constellation and multi-frequency GNSS data grabber and a software-defined radio receiver. Having access to the full receiver chain and to intermediate signal processing stages allows a deep analysis of the impact of scintillation and, in turn, a better understanding of the physical phenomenon. The possibility to process high-resolution raw intermediate frequency samples of the signal enables not only the computation of scintillation indexes with the same quality as professional devices but also the design and test of innovative receiver architectures and algorithms. Furthermore, the record and replay approach offers the possibility to process in the lab the signals captured on site, with high fidelity level. It is like being in Antarctica again, but with an unlimited set of receivers and higher computational, storage, and bandwidth resources. The main advantages and disadvantages of this approach are analyzed. Examples of monitoring results are reported, confirming the monitoring capabilities, showing the good agreement with commercial receiver outputs and confirming the validity of post-processing and re-play operations

Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax

This work focuses on a machine learning based detection of iono-spheric scintillation events affecting Global Navigation Satellite System (GNSS) signals. We here extend the recent detection results based on Decision Trees, designing a semi-supervised detection system based on the DeepInfomax approach recently proposed. The paper shows that it is possible to achieve good classification accuracy while reducing the amount of time that human experts must spend manually labelling the datasets for the training of supervised algorithms. The proposed method is scalable and reduces the required percentage of annotated samples to achieve a given performance, making it a viable candidate for a realistic deployment of scintillation detection in software defined GNSS receivers

Detection of GNSS Ionospheric Scintillations based on Machine Learning Decision Tree

This paper proposes a methodology for automatic, accurate and early detection of amplitude ionospheric scintillation events, based on machine learning algorithms, applied on big sets of 50 Hz post-correlation data provided by a GNSS receiver. Experimental results on real data show that this approach can considerably improve traditional methods, reaching a detection accuracy of 98%, very close to human-driven manual classification. Moreover, the detection responsiveness is enhanced, enabling early scintillation alerts

Effects of Phase Scintillation on the GNSS Positioning Error During the September 2017 Storm at Svalbard

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Installation and configuration of an ionospheric scintillation monitoring station based on GNSS SDR receivers in Brazil

The use of Global Navigation Satellite Systems (GNSSs) is nowadays very popular, and the positioning service that they provide is becoming the basis of several applications. Due to their wide coverage, GNSS signals can be used at no cost as probing signals to retrieve parameters to characterize the atmosphere, such as ionospheric scintillation indexes. GNSS receivers coupled to the specific algorithm are indeed a valid alternative to large and expensive ad hoc equipment such as ionosondes. In particular, Software Defined Radio (SDR) receivers are characterized by a higher level of flexibility and configurability when compared to commercial receivers, which fits for the purposes of ionospheric monitoring and enable the study of advanced and innovative algorithms, both for scientific purposes (ionospheric monitoring, space weather), and for technological development (robust GNSS receivers design). A GNSS-based ionosphere monitoring station, including an SDR-based receiver and a professional receiver, was installed in the CRAAM laboratory at Mackenzie Presbyterian University (São Paulo, Brazil) on May 2017. Details of the installation and the new approaches for the storage, processing, and transfer of GNSS data, including raw Intermediate Frequency (IF) samples, are described, along with preliminary results related to ionospheric events captured during the first months of its operation

Survey on Signal Processing for GNSS under Ionospheric Scintillation: Detection, Monitoring, and Mitigation

Ionospheric scintillation is the physical phenomena affecting radio waves coming from the space through the ionosphere. Such disturbance is caused by ionospheric electron density irregularities and is a major threat in Global Navigation Satellite Systems (GNSS). From a signal processing perspective, scintillation is one of the most challenging propagation scenarios, particularly affecting high-precision GNSS receivers and safety critical applications where accuracy, availability, continuity and integrity are mandatory. Under scintillation, GNSS signals are affected by amplitude and phase variations, which mainly compromise the synchronization stage of the receiver. To counteract these effects, one must resort to advanced signal processing techniques such as adaptive/robust methods, machine learning or parameter estimation. This contribution reviews the signal processing landscape in GNSS receivers, with emphasis on different detection, monitoring and mitigation problems. New results using real data are provided to support the discussion. To conclude, future perspectives of interest to the GNSS community are discussed

Disentangling ionospheric refraction and diffraction effects in GNSS raw phase through fast iterative filtering technique

We contribute to the debate on the identification of phase scintillation induced by the ionosphere on the global navigation satellite system (GNSS) by introducing a phase detrending method able to provide realistic values of the phase scintillation index at high latitude. It is based on the fast iterative filtering signal decomposition technique, which is a recently developed fast implementation of the well-established adaptive local iterative filtering algorithm. FIF has been conceived to decompose nonstationary signals efficiently and provide a discrete set of oscillating functions, each of them having its frequency. It overcomes most of the problems that arise when using traditional time–frequency analysis techniques and relies on a consolidated mathematical basis since its a priori convergence and stability have been proved. By relying on the capability of FIF to efficiently identify the frequencies embedded in the GNSS raw phase, we define a method based on the FIF-derived spectral features to identify the proper cutoff frequency for phase detrending. To test such a method, we analyze the data acquired from GPS and Galileo signals over Antarctica during the September 2017 storm by the ionospheric scintillation monitor receiver (ISMR) located in Concordia Station (75.10° S, 123.33° E). Different cases of diffraction and refraction effects are provided, showing the capability of the method in deriving a more accurate determination of the σϕ index. We found values of cutoff frequency in the range of 0.73–0.83 Hz, providing further evidence of the inadequacy of the choice of 0.1 Hz, which is often used when dealing with ionospheric scintillation monitoring at high latitudes