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.

In recent years, the push for accurate and reliable time synchronization has become increasingly important in crit?ical infrastructure, particularly in telecommunication networks. The enhanced performance of 5G New Radio and next-generation technologies rely on phase synchronization of Radio Access Network (RAN) nodes, which require sub-microsecond relative timing errors. Atomic clocks, integrated with Global Navigation Satellite Systems (GNSS) timing receivers, have been deployed in timing networks as Grand Master Clocks (GMCs). However, this solution does not scale well with the growing number of interme?diate nodes in current RANs. A more affordable and distributed solution is needed for scalability and time synchronization. GNSS timing receivers are a cost-effective solution providing stable reference clock signals, but a proliferation of GNSS antennas can expose the network to malicious radio-frequency attacks. This research proposes a solution for stable and resilient GNSS?based network synchronization, using the White Rabbit Precise Time Protocol and a timing source backup logic in case of timing-disruptive attacks. The solution was tested against popular jamming, meaconing, and spoofing attacks and was able to maintain 2 ns relative synchronization accuracy between its nodes under any of the tested attacks, without the support of an atomic clock.

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 the ionosphere. The latter can be pursued by processing the GNSS signals through dedicated ground-based monitoring equipment, such as the GNSS Ionospheric Scintillation and Total Electron Content 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 signal bands, observed and captured through an experimental, software defined radio setup. The paper also highlights the adverse impacts of the interferer on the amplitude scintillation indices employed in scientific investigations, and presents a methodology to discriminate among regular and corrupted scintillation data. To support further investigations, a dataset of baseband signals samples affected by the RFI is available at IEEE DataPort.

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.

This letter presents a methodology to reduce the computational burden of Particle Filters (PF) used to achieve a target state estimation accuracy in Bayesian tracking problems. A strategy, named Multiple Weighting (MW), that exploits information diversity of the input measurements for an efficient weighting of the particles is introduced. This study shows that, by relying on the prior knowledge of the state-observations relationships, it is possible to achieve a significant reduction in the number of samples required to obtain any target accuracy. An experimental assessment is provided with an application to precise positioning estimation in Global Navigation Satellite System (GNSS) receivers.

In recent years positioning and navigation capabilities in mobile devices have become essential to the evergrowing number of position-related smart applications. Global Navigation Satellite System (GNSS) constitutes the provider for geo-localization, therefore consumer-grade, embedded GNSS receivers have become ubiquitous in mobile smart devices. Among these, smartphones play a dominant role in enabling such modern services based on position information. However, GNSS positioning shows several weaknesses in urban contexts where mobile smart devices are massively diffused. Indeed, the limited sky visibility and multipath scattering induced by buildings severely threat the quality of the final solution. Two main ingredients can enable innovative collaborative strategies capable to increase the robustness of GNSS navigation: The availability of raw GNSS measurements which have been recently disclosed in ultra-low cost smartphone chipsets and the ubiquitous connectivity provided by modern low-latency, network infrastructures allowing for near-real-time exchange of data. This work presents the architecture of a Proof Of Concept designed to demonstrate the feasibility of a GNSS-only Cooperative Positioning among networked smartphones equipped with GNSS receivers. The test campaign presented in this work assessed the feasibility of the approach over 4G/LTE network connectivity and an average accuracy improvement over the 40%.