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

Analysis of multi-constellation GNSS PPP solutions under phase scintillations at high latitudes

In the past few years, the rapid evolution of multi-constellation navigation satellite systems boosted the development of many scientific and engineering applications. More than 100 satellites will be available in a few years, when all the four global constellations (GPS, GLONASS, Galileo, and Beidou) will be fully deployed. This high number of visible satellites has improved the performance of precise point positioning (PPP) techniques both in terms of accuracy and of session length, especially easing the modeling of ionospheric biases. However, in the presence of severe environmental and atmospheric conditions, the performance of PPP considerably deteriorates. It is the case of high-latitude scenarios, where the satellites coverage is limited, the satellites geometry is poor and ionospheric scintillation are frequent. This paper analyzes the quality of PPP solutions in terms of accuracy and convergence time, for a GNSS station in Antarctica. Single and multi-constellation results are compared, proving the benefits of the availability of a higher number of satellites as well as the improved robustness to the presence of moderate and strong phase scintillations. The use of PPP multi-constellation at high latitudes is indeed essential to guarantee high accuracy, and to obtain a low convergence time, of the order of tens of minutes

The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines

Scintillation caused by the electron density irregularities in the ionospheric plasma leads to rapid fluctuations in the amplitude and phase of the Global Navigation Satellite Systems (GNSS) signals. Ionospheric scintillation severely degrades the performance of the GNSS receiver in the signal acquisition, tracking, and positioning. By utilizing the GNSS signals, detecting and monitoring the scintillation effects to decrease the effect of the disturbing signals have gained importance, and machine learning-based algorithms have been started to be applied for the detection. In this paper, the performance of Support Vector Machines (SVM) for scintillation detection is discussed. The effect of the different kernel functions, namely, linear, Gaussian, and polynomial, on the performance of the SVM algorithm is analyzed. Performance is statistically assessed in terms of probabilities of detection and false alarm of the scintillation event. Real GNSS signals that are affected by significant phase and amplitude scintillation effect, collected at the South African Antarctic research base SANAE IV and Hanoi, Vietnam have been used in this study. This paper questions how to select a suitable kernel function by analyzing the data preparation, cross-validation, and experimental test stages of the SVM-based process for scintillation detection. It has been observed that the overall accuracy of fine Gaussian SVM outperforms the linear, which has the lowest complexity and running time. Moreover, the third-order polynomial kernel provides improved performance compared to linear, coarse, and medium Gaussian kernel SVMs, but it comes with a cost of increased complexity and running time

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

Ionospheric scintillation monitoring and modelling

This paper presents a review of the ionospheric scintillation monitoring and modelling by the European groups involved in COST 296. Several of these groups have organized scintillation measurement campaigns at low and high latitudes. Some characteristic results obtained from the measured data are presented. The paper also addresses the modeling activities: four models, based on phase screen techniques, with different options and application domains are detailed. Finally some new trends for research topics are given. This includes the wavelet analysis, the high latitudes analysis, the construction of scintillation maps and the mitigation techniques

A Comparative Performance Analysis of GPS L1 C/A, L5 Acquisition and Tracking Stages under Polar and Equatorial Scintillations

This paper provides a comparative performance analysis of different acquisition and tracking methods of GPS L1 C/A and GPS L5 signals testing their robustness to the presence of scintillations in the propagation environment. The paper compares the different acquisition methods in terms of probabilities of detection/false alarm, peak-to-noise floor ratios for the acquired signal and execution time, assessing the performance loss in the presence of scintillations. Moreover, robust tracking architectures that are optimized to operate in a harsh ionospheric environment have been employed. The performance of the carrier tracking methods, namely, traditional Phase-Locked Loop (PLL) and Kalman filter based-PLL, have been compared in terms of the standard deviation of Doppler estimation, phase error, phase lock indicator (PLI) and phase jitter. The study is based on real GNSS signals affected by significant phase and amplitude scintillation effects, collected at the South African Antarctic research base (SANAE IV) and Brazilian Centro de Radioastronomia e Astrofisica Mackenzie (CRAAM) monitoring stations. Performance is assessed exploiting a fully software GNSS receiver which implements the different architectures. The comparative analysis allows to choose the best setting of the acquisition and tracking parameters, in order to allow the operation of signal acquisition and tracking at a required performance level under scintillation conditions

An Open Architecture for Signal Monitoring and Recording Based on SDR and Docker Containers: A GNSS Use Case

Signal monitoring and recording station architectures based on software-defined radio (SDR) have been proposed and implemented since several years. However, the large amount of data to be transferred, stored, and managed when high sampling frequency and high quantization depth are required, poses a limit to the performance, mostly because of the data losses during the data transfer between the front-end and the storage unit. To overcome these limitations, thus allowing a reliable, high-fidelity recording of the signals as required by some applications, a novel architecture named SMART (Signal Monitoring, Analysis and Recording Tool) based on the implementation of Docker containers directly on a Network Attached Storage (NAS) unit is presented. Such paradigms allow for a fully open-source system being more affordable and flexible than previous prototypes. The proposed architecture reduces computational complexity, increases efficiency, and provides a compact, cost-effective system that is easy to move and deploy. As a case study, this architecture is implemented to monitor Radio-Frequency Interferences (RFI) on Global Navigation Satellite System (GNSS) L1/E1 and L5/E5 bands. The sample results show the benefits of a stable, long-term capture at a high sampling frequency to characterize the RFIs spectral signature effectively

Analysis and Detection of Outliers in GNSS Measurements by Means of Machine Learning Algorithms

L'abstract è presente nell'allegato / the abstract is in the attachmen

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

GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype

The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector