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In this study, the concept of multichannel signal tracking, previously discussed in the Global Navigation Satellite System literature, is expanded towards next-generation satellite navigation paradigms by leveraging multiple narrowband signals that are synchronously transmitted from the same satellite. This approach achieves a performance comparable to that of signals transmitted over larger bandwidths while effectively reallocating frequency resources to enhance the capacity of a wideband communication channel. This methodology can be applied to orthogonal signals, independently from their respective modulations and regardless of whether they are transmitted over different carrier frequencies or occupy the same bandwidth. The resulting architecture is tested in modern positioning, navigation and timing (PNT) scenarios, addressing in particular, the forthcoming low earth orbit (LEO) PNT paradigm to broad the technique’s applicability also towards potential piggybacked services hosted by broadband megaconstellations (e.g. Kuiper, Starlink). The applicability domain of the signal tracking architecture is explored through a parametric analysis, assessing the performance under different combined conditions such as transmission bands, multichannel combination modes, Doppler profiles, and propagation conditions. This technique has shown large performance improvements with respect to conventional single channel processing, yielding code estimation error reductions above 90% in almost all the tested conditions.

Abstract Multilevel and multicarrier component signals are now common in many Global Navigation Satellite Systems challenging the employed multiplexing method that needs to be more flexible and powerful. In this work, the authors demonstrate how, by acting on two parameters of the digital baseband representation of component signals (the sampling frequency and the central frequency of the baseband complex envelope), it is possible to optimise the performance of the multiplexer, while still obtaining a composite signal that fulfils the required system constraints.

In the near future, international space agencies have planned to achieve significant milestones in investigating the utilization of Global Navigation Satellite Systems (GNSS) within and beyond the current space service volume up to their application to lunar missions. These initiatives aim to demonstrate the feasibility of GNSS navigation at lunar altitudes. Based on the outcomes of such demonstrations, dozens of lunar missions will likely be equipped with a GNSS receiver to support autonomous navigation in the lunar proximity. Relying on non-invasive, consolidated differential techniques, GNSS will enable baseline estimation, thus supporting a number of potential applications to lunar orbiters such as collaborative navigation, formation flight, orbital manoeuvers, remote sensing, augmentation systems and beyond. Unfortunately, the large dynamics and the geometry of such differential GNSS scenarios set them apart from current terrestrial and low-earth orbit use cases. These characteristics result in an increased sensitivity to measurements time misalignment among orbiters. Hence, this paper offers a review of baseline estimation methods and characterizes the divergences and limitations w.r.t. to terrestrial applications. The study showcases the estimation of the baseline length between a lunar CubeSat mission, VMMO, and the communication relay Lunar Pathfinder mission. Notably, real GNSS measurements generated by an Engineering Model of the NaviMoon receiver in the European Space Agency (ESA/ESTEC) Radio Navigation Laboratory are utilized. A radio-frequency constellation simulator is used to generate the GNSS signals in these hardware-in-the-loop tests. The performed analyses showed the invalidity of common terrestrial differential GNSS ranging techniques for space scenarios due to the introduction of significant biases. Improved ranging algorithms were proposed and their potential to cancel ranging errors common to both receivers involved was confirmed.

Taking the work conducted by the global navigation satellite system (GNSS) software-defined radio (SDR) working group during the last decade as a seed, this contribution summarizes, for the first time, the history of GNSS SDR development. This report highlights selected SDR implementations and achievements that are available to the public or that influenced the general development of SDR. Aspects related to the standardization process of intermediate-frequency sample data and metadata are discussed, and an update of the Institute of Navigation SDR Standard is proposed. This work focuses on GNSS SDR implementations in general-purpose processors and leaves aside developments conducted on field programmable gate array and application-specific integrated circuit platforms. Data collection systems (i.e., front-ends) have always been of paramount importance for GNSS SDRs and are thus partly covered in this work. This report represents the knowledge of the authors but is not meant as a complete description of SDR history.

Due to the low received power of Global Navigation Satellite Signals (GNSS), the performance of GNSS receivers can be disrupted by anthropogenic radio frequency interferences, with intentional jamming and spoofing activities being among the most critical threats. It is reported in the literature that modern, GNSS-equipped Android smartphones are generally resistant to simplistic spoofing, and many recent contributions support such a biased belief. In this paper, we present the results of a test campaign designed to further stress the resilience of such devices to simplistic spoofing attacks and highlight their actual vulnerability. We then propose an effective spoofing detection technique, that exploits the spatial and temporal correlation of the counterfeit signals by leveraging the statistical analysis of raw GNSS measurements. By not requiring access to the low signal processing level of the GNSS receiver, the proposed solution applies to any device embedding a GNSS receiver that provides raw GNSS measurements, such as current Android smartphones. Vulnerability analysis and validation of the proposed technique were conducted in a controlled environment by transmitting realistic, counterfeit Global Positioning System L1/CA navigation signals to a variety of Android smartphones embedding also different GNSS chipsets. We show that, under proper conditions, the devices were vulnerable to the attacks and that the effects were visible through their raw measurements, i.e., Carrier-to-noise ratio $(C/N_{0})$ , pseudo-range measurements, and position estimates. In particular, the study demonstrates that cross-correlation between the $C/N_{0}$ time series provided by each device for different GNSS satellites increases under spoofing conditions, thus constituting an effective metric to detect the attack within a few seconds.

Radio frequency (RF) signals transmitted by Global Navigation Satellite Systems (GNSSs) 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. This article 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.

Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth.

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.

In the last decades, the increasing complexity of the fusion of proprioceptive and exteroceptive sensors with Global Navigation Satellite System (GNSS) has motivated the exploration of Artificial Intelligence related strategies for the implementation of the navigation filters. In order to meet the strict requirements of accuracy and precision for Intelligent Transportation Systems (ITS) and Robotics, Bayesian inference algorithms are at the basis of current Positioning, Navigation, and Timing (PNT). Some scientific and technical contributions resort to Sequential Importance Resampling (SIR) Particle Filters (PF) to overcome the theoretical weaknesses of the more popular and efficient Kalman Filters (KFs) when the application relies on non-linear measurements models and non-Gaussian measurements errors. However, due to its higher computational burden, SIR PF is generally discarded. This paper presents a methodology named Multiple Weighting (MW) that reduces the computational burden of PF by considering the mutual information provided by the input measurements about the unknown state. An assessment of the proposed scheme is shown through an application to standalone GNSS estimation as a baseline of more complex multi-sensors, integrated solutions. By relying on the a-priori knowledge of the relationship between states and measurements, a change in the conventional PF routine allows performing a more efficient sampling of the posterior distribution. Results show that the proposed strategy can achieve any desired accuracy with a considerable reduction in the number of particles. Given a fixed and reasonable available computational effort, the proposed scheme allows for an accuracy improvement of the state estimate in the range of 20–40%.

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.

The advances in low-latency communications networks and the ever-growing amount of devices offering localization and navigation capabilities opened a number of opportunities to develop innovative network-based collaborative solutions to satisfy the increasing demand for positioning accuracy and precision. Recent research works indeed, have fostered the concept of networked Global Navigation Satellite System (GNSS) receivers supporting the sharing of raw measurements with other receivers within the same network. Such measurements (i.e. pseudorange and Doppler) can be processed through Differential GNSS (DGNSS) techniques to retrieve inter-agent distances which can be in turn integrated to improve positioning performance. This article investigates an improved Bayesian estimation algorithm for a sensorless, tight-integration of DGNSS-based collaborative measurements through a modified Particle Filter (PF), namely Cognitive PF. Differently from Extended Kalman Filter and Uscented Kalman Filter indeed, a PF natively support the non-Gaussian noise distribution which characterizes DGNSS-based inter-agent distances. The proposed Cognitive PF is hence designed, implemented and optimized according to the architecture of a proprietary Inertial Navigation System (INS)-free Global Navigation Satellite System (GNSS) software receiver. Experimental tests performed through realistic radio-frequency GNSS signals showed a remarkable improvement in positioning accuracy w.r.t. reference PF and EKF architectures.

For the commercial sector, warehouses are becoming increasingly vital. Constant efforts are in progress to increase the efficiency of these facilities while reducing costs. The inventory part of the goods is a time-consuming task that impacts the company’s revenue. This article presents an analysis of the performance of a state-of-the-art, visual-inertial odometry algorithm, SVO Pro Open, when varying the resolution and frequency of video streaming in an industrial environment. To perform efficiently this task, achieving an optimal system in terms of localization accuracy, robustness, and computational cost is necessary. Different resolutions are selected with a constant aspect ratio, and an accurate calibration for each resolution configuration is performed. A stable operating point in terms of robustness, accuracy of localization, and CPU utilization is found and the trends obtained are studied. To keep the system robust against sudden divergence, the feature loss factor extracted from optical sensors is analyzed. Innovative trends and translation errors on the order of a few tens of centimeters are achieved, allowing the system to navigate safely in the warehouse. The best result is obtained at a resolution of 636 × 600 px, where the localization errors (x, y, and z) are all under 0.25 m. In addition, the CPU (Central Processing Unit) usage of the onboard computer is kept below 60%, remaining usable for other relevant onboard processing tasks.

In the last few years, all countries worldwide have fought the spread of SARS-CoV-2 (COVID-19) by exploiting Information and Communication Technologies (ICT) to perform contact tracing. In parallel, the pandemic has highlighted the relevance of mobility and social distancing among citizens. The monitoring of such aspects appeared prominent for reactive decision-making and the effective tracking of the infection chain. In parallel to the proximity sensing among people, indeed, the concept of social distancing has captured the attention to signal processing algorithms enabling short-to-medium range distance estimation to provide behavioral models in the emergency. By exploiting the availability of smart devices, the synergy between mobile network connectivity and Global Navigation Satellite Systems (GNSS), cooperative ranging approaches allow computing inter-personal distance measurements in outdoor environments through the exchange of light-weight navigation data among interconnected users. In this paper, a model for Inter-Agent Ranging (IAR) is provided and experimentally assessed to offer a naive collaborative distancing technique that leverages these features. Although the technique provides distance information, it does not imply the disclosure of the user’s locations being intrinsically prone to protect sensitive user data. A statistical error model is presented and validated through synthetic simulations and real, on-field experiments to support implementation in GNSS-equipped mobile devices. Accuracy and precision of IAR measurements are compared to other consolidated GNSS-based techniques showing comparable performance at lower complexity and computational effort.

This paper describes a bioinspired neural-network-based approach to solve a coverage planning problem for a fleet of unmanned aerial vehicles exploring critical areas. The main goal is to fully cover the map, maintaining a uniform distribution of the fleet on the map, and avoiding collisions between vehicles and other obstacles. This specific task is suitable for surveillance applications, where the uniform distribution of the fleet in the map permits them to reach any position on the map as fast as possible in emergency scenarios. To solve this problem, a bioinspired neural network structure is adopted. Specifically, the neural network consists of a grid of neurons, where each neuron has a local cost and has a local connection only with neighbor neurons. The cost of each neuron influences the cost of its neighbors, generating an attractive contribution to unvisited neurons. We introduce several controls and precautions to minimize the risk of collisions and optimize coverage planning. Then, preliminary simulations are performed in different scenarios by testing the algorithm in four maps and with fleets consisting of 3 to 10 vehicles. Results confirm the ability of the proposed approach to manage and coordinate the fleet providing the full coverage of the map in every tested scenario, avoiding collisions between vehicles, and uniformly distributing the fleet on the map.

Improving the Global Navigation Satellite System (GNSS) receiver robustness in a radio interfered environment has been always one of the main concerns for the GNSS community. Due to the weakness of the signal impinging the GNSS receiver antenna, GNSS receiver performance can be seriously threatened by the presence of stronger interfering signals. In these scenarios, classical interference countermeasures may fail due to the fact that interference detection and removal process causes also a non-negligible degradation of the received GNSS signal. This paper introduces an innovative interference detection and mitigation technique against the well-known jamming threat. This technique is based on the use of the Karhunen–Loève Transform (KLT) which allows for the representation of the received interfered signals in a transformed domain where interference components can be better identified, isolated and removed, avoiding significant degradation of the useful GNSS signal.

This work focuses on a machine learning based detection of ionospheric 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.

Signals from global navigation satellite systems (GNSS) can be utilized as signals of opportunity in remote sensing applications. Geophysical properties of the earth surface can be detected and monitored by processing the back-scattered GNSS signals from the ground. In the literature, several airborne GNSS-based passive radar experiments have been successfully demonstrated. With the advancements in small unmanned aerial vehicles (UAVs) and their applications for environmental monitoring, we want to investigate whether GNSS-based passive radar can provide valuable geospatial information from such platforms. Low-cost GNSS reflectometry sensors, developed using commercial of the shelf components, can be mounted onboard UAVs and flown to sense environmental parameters. This paper presents the results of a preliminary study to investigate the feasibility of utilizing data collected by UAV-based GNSS-R sensors to detect surface water for a potential application in supporting flood monitoring operations. The study was conducted in the area surrounding the Avigliana lakes in Northern Italy. The results show the possibility of detecting small water surfaces with few tens of meters resolution, and estimating the area of the lake surface with 92% accuracy. Furthermore, it is proved through simulations that the use of multi-GNSS increases this accuracy to about 99%.

The quality of positioning services based on Global Navigation Satellite Systems (GNSS) is improving at a fast pace, driven by the strict requirements of a plethora of new applications on accuracy, precision and reliability of the services. Nevertheless, ionospheric errors still bound the achievable performance and better mitigation techniques must be devised. In particular, the harmful effect due to non-uniform distribution of the electron density that causes amplitude and phase variation of the GNSS signal, usually named as scintillation effects. For many high-accuracy applications, this is a threat to accuracy and reliability, and the presence of scintillation effect needs to be constantly monitored. To this purpose, traditional receivers employ closed-loop tracking architectures. In this paper, we investigate an alternative architecture and a related metric based on the statistical processing of the received signal, after a code-wipe off and a noise reduction phase. The new metric is based on the analysis of the statistical features of the conditioned signal, and it brings the same information of the S 4 index, normally estimated by means of closed-loop receivers. This new metric can be obtained at a higher rate as well as in the case of strong scintillations when a closed-loop receiver would fail the tracking of the GNSS signals.

Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

The paper investigates approaches for loosely coupled GPS/INS integration. Error performance is calculated using a reference trajectory. A performance improvement can be obtained by exploiting additional map information (for example, a road boundary). A constrained solution has been developed and its performance compared with an unconstrained one. The case of GPS outages is also investigated showing how a Kalman filter that operates on the last received GPS position and velocity measurements provides a performance benefit. Results are obtained by means of simulation studies and real data.

In this paper, multipath error on Global Navigation Satellite System (GNSS) signals in urban environments is characterized with the help of Light Detection and Ranging (LiDAR) measurements. For this purpose, LiDAR equipment and Global Positioning System (GPS) receiver implementing a multipath estimating architecture were used to collect data in an urban environment. This paper demonstrates how GPS and LiDAR measurements can be jointly used to model the environment and obtain robust receivers. Multipath amplitude and delay are estimated by means of LiDAR feature extraction and multipath mitigation architecture. The results show the feasibility of integrating the information provided by LiDAR sensors and GNSS receivers for multipath mitigation.

Global navigation satellite systems play a significant role in the development of intelligent transport systems, where the estimation of the vehicle’s position is a key element. However, in strongly constrained environments such as city centers, the definition of quality metrics and the assessment of positioning performances are challenges to be addressed. Due to the variability of different urban scenarios, the modeling of the dynamics as well as the architecture of the positioning platform, which might embed other sensors and aiding means to the GNSS unit, make it hard to define unambiguous positioning metrics. Performance assessment through analytical models and simulators can be ineffective in terms of cost, complexity, and general validity and scalability of the results. This paper shows how a record and replay approach can be an efficient solution to grant fidelity to a realistic scenario. This work discusses advantages and disadvantages with emphasis on the case study of harsh scenarios. Such an approach requires proper data collections that allow the replay phase to test the GNSS-based positioning terminals. This paper presents the results obtained on a set of field tests related to different scenarios, selected as representative for the key performance indicators assessment.

In this paper, the Extended Coupled Amplitude Delay Lock Loop (ECADLL) architecture, previously introduced as a solution able to deal with a multipath environment, is revisited and improved to tailor it to spoofing detection purposes. Exploiting a properly-defined decision algorithm, the architecture is able to effectively detect a spoofer attack, as well as distinguish it from other kinds of interference events. The new algorithm is used to classify them according to their characteristics. We also introduce the use of a ratio metric detector in order to reduce the detection latency and the computational load of the architecture.