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Global Navigation Satellite Systems (GNSS) are one of the most important infrastructures in the modern world, also enabling many critical applications that require the reliability of the received signals. However, it is well known that the power of the GNSS signals at the receiver's antenna is extremely weak, and radio-frequency interference affecting the GNSS bandwidths might lead to reduced positioning and timing accuracy or even a complete lack of the navigation solution. Therefore, in order to mitigate interference in the GNSS receivers and guarantee reliable solutions, interference detection and classification becomes of paramount importance. This paper proposes an approach for the automatic and accurate detection and classification of the most common interference and jammers based on the use of Convolutional Neural Networks (CNN). The input for the network is the visual time-frequency representation of the received signal, together with statistical features in the time and frequency domains. The time-frequency representation is obtained using both the Wigner-Ville and the short-time Fourier transforms. Moreover, the performance of the proposed method is compared using two different CNN architectures, AlexNet and ResNet. The effectiveness of the method is shown in two case studies: Monitoring and classification by a terrestrial monitoring station and from a Low Earth Orbit satellite (LEO). The results show that the proposed method has quite a high accuracy in detecting and classifying interference, even with low power, and can be implemented as a real-time tool for monitoring jammers.

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 RFIs 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.

Radio Frequency (RF) signals transmitted by Global Navigation Satellite Systems (GNSS) are exploited as signals of opportunity in many scientific activities, ranging from sensing waterways and humidity of the terrain to the monitoring of ionospheric irregularities. The latter can be pursued by processing the GNSS signals through dedicated ground-based monitoring equipment, such as the GNSS Ionospheric Scintillation and TEC Monitoring (GISTM) receivers. Nonetheless, GNSS signals are susceptible to intentional or unintentional RF interferences (RFIs), which may alter the calculation of the scintillation indices, thus compromising the quality of the scientific data and the reliability of the derived space weather monitoring products. Upon the observation of anomalous scintillation indices computed by a GISTM receiver in the Mediterranean area, the study presents the results of the analysis and characterization of a deliberate, unclassified interferer acting on the L1/E1 GNSS frequencies, captured through an experimental setup based on a Software Defined Radio (SDR) architecture. The paper also hilights the adverse impacts of the interferer on the scintillation indices employed in scientific investigations. Additionally, a dataset of Intermediate Frequency (IF) signals samples affected by the RFI will be available at IEEE DataPort upon publication.

Tight integration algorithms fusing Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) have become popular in many high-accuracy positioning and navigation applications. Despite their reliability, common integration architectures can still run into accuracy drops under challenging navigation settings. The growing computational power of low-cost, embedded systems has allowed for the exploitation of several advanced Bayesian state estimation algorithms, such as the Particle Filter (PF) and its hybrid variants, e.g. Unscented Particle Filter (UPF). Although sophisticated, these architectures are not immune from multipath scattering and Non-Line-of-Sight (NLOS) signal receptions, which frequently corrupt satellite measurements and jeopardise GNSS/INS solutions. Hence, a certain level of modelling adaptivity should be granted to avoid severe drifts in the estimated states. Given these premises, the paper presents a novel Adaptive Unscented Particle Filter (AUPF) architecture leveraging two cascading stages to cope with disruptive, biased GNSS input observables in harsh conditions. A INS-based signal processing block is implemented upstream of a Redundant Measurement Noise Covariance Estimation (RMNCE) stage to strengthen the adaptation of observables’ statistics and improve the state estimation. An experimental assessment is provided for the proposed robust AUPF that demonstrates a 10 % average reduction of the horizontal position error above the 75-th percentile. In addition, a comparative analysis both with previous adaptive architectures and a plain UPF is carried out to highlight the improved performance of the proposed methodology.

In urban contexts, the increasing density of electronic devices equipped with Global Navigation Satellite System (GNSS) receivers and complementary positioning technologies is attracting research and development efforts devoted to an improvement of the quality of life towards the smart city paradigm. Vehicular and pedestrian positioning and navigation capabilities are among the major drivers for innovation in this process. Ultra-low-cost electronics such as smartphones and Internet of Things (IoT) sensors aim at providing accurate and reliable positioning solutions through a set of promising solutions. Among these, snapshot positioning allows to remotely perform the post-processing of GNSS signals in IoT sensor networks while Wi-Fi&trade, ranging and cooperative positioning provide auxiliary anchors of opportunity to enhance indoor/outdoor positioning capabilities. This paper presents an innovative platform to perform a centralised testing and assessment of such positioning and navigation technologies along with a set of results obtained in the context of the European project HANSEL, by relying on current network technologies and infrastructures (i.e., Wi-Fi&trade, and cellular connectivity).

International audience; 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.

Tight integration of Global Navigation Satellite System (GNSS) and Ultra-Wide Band (UWB) is growing popularity as a positioning strategy relying on mass-market devices for applications dealing with localisation and tracking of agents in challenging environments. However, due to independent clocks of Commercial off-the-shelf (COTS) devices, the offset between the time-scales with respect to which measurements are time tagged must be mitigated to achieve accurate state-estimation via centralised, recursive filtering architectures. In this paper, it is analysed a GNSS/UWB Tightly-Coupled (TC) scheme based on an Extended Kalman Filter (EKF), and the integration of asynchronous GNSS/UWB measurements is investigated to highlight how it can induce state-estimation errors under different receiver kinematics. GNSS/UWB time calibration is addressed as a filtering problem, for which an EKF-based framework is developed to recursively model the GNSS/UWB time-offset as a further unknown in the system state-space model. Moreover, a double-update filtering model is proposed with embedded optimisations for the adaptive tuning of UWB ranging statistics. Experimental results with simulated data show that the double-update EKF algorithm can achieve a horizontal positioning accuracy gain 41.60 % over a plain EKF integration with uncalibrated time-offset and of 15.43 % over the EKF with naive time-offset calibration.