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Global Navigation Satellite Systems (GNSS) spoofing is a pernicious type of intentional interference where a GNSS receiver is fooled into tracking counterfeit signals, with the purpose of inducing a misleading information into the application it is used for. A possible technique able to identify a GNSS spoofing attack is a dual-antenna method based on the analysis of the dispersion of the double differences (D3) of carrier-phase measurements. Such technique has been recently presented as a relatively simple method to detect situations where even only a subset of counterfeit signals is tracked by the receiver. However, the D3 technique has not been analyzed in a rigorous theoretical way so far and the detection threshold was, for instance, set only empirically. Aiming at filling these gaps, this article intends to revise the main concepts of the aforementioned technique in a clear mathematical way. Thus, the detection threshold will be given according to a target probability of missed detection. Moreover, the article provides a thorough analysis of expected performance in terms of probability of missed detection and probability of false alarm, addressing them first as pairwise probability, then as overall probability. The effect of the signal C/N0 ratio on these detection performances is analyzed. Methods to reduce the occurrence of events of false alarm are also discussed. Eventually, an assessment of performance of the ${{\rm{D}}^3}$ algorithm is evaluated through a set of tests that emulate real working conditions.

This article 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. This article 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 article is based on real global navigation satellite systems (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.

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.

Precision Agriculture (PA) refers to applications asking for reliable and highly available precise positions, at centimeter level, in most of operational scenarios. Machinery guidance, automatic steering and controlled traffic farming enable machinery to move along repeatable tracks on the field, minimizing pass-to-pass errors and overlaps. In the recent years, satellite-based navigation has also opened the door to (semi) autonomous machineries for some specific farming scenarios and operations. Farming industry is now looking to use small robots to bring efficiencies and benefits to farms, capable of complex tasks that have not been possible with traditional large-scale agricultural machinery. Even though the state-of-the-art Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) receivers usually match the requirements posed by PA applications in open fields, propagation effects degrade the performance under foliage or with surrounding obstacles. This paper presents an experimental testbed and methodology suitable to assess the real performance of RTK GNSS-based devices in operational environments. Such testbed and methodology were effective to compare different devices, which resulted to be equivalent in open-sky conditions, but with significant differences in other types of environments. The paper also discusses opportunities and current limits of GNSS for emerging PA applications based on small robots and artificial intelligence.

Several navigation filters have been developed since the early steps of Global Navigation Satellite Systems (GNSS) to provide high-accuracy Positioning, Navigation and Timing (PNT), and many solutions are available in the literature to support a plethora of applications. In the context of vehicular navigation and positioning, advanced state estimation and sensors fusion techniques cannot cope by themselves with strong multipath effects in dense urban areas. Therefore, these solutions require more robust approaches typically involving an additional processing effort, especially in low-cost Inertial Navigation System (INS)/GNSS Tightly-Coupled (TC) integration scheme. This work analyzes state-of-the-art covariance matrix estimation methods and proposes an INS-based pre-processing stage to mitigate the impact of undesired, multipath-related bias injections without inhibiting the Inertial Navigation System (INS)/GNSS integration. The proposed adaptive solution improves the overall stability and estimation accuracy of a set of Bayesian filters, i.e., Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Unscented Particle Filter (UPF). Results are presented about a real dataset in which an advanced, state-of-the-art Adaptive UPF (AUPF) TC scheme, applied to a low-cost integrated setup, occasionally failed to track the navigation solution due to poorly conditioned GNSS measurements.

This paper presents the development of a new metric to improve the performance of the Dispersion of Double Differences (D3) algorithm, which detects GNSS spoofing attacks with a dual-antenna system. The new metric is based on a linear regression applied to the fractional phase double differences. The original D3 algorithm is sometimes prone to false alarms and to missed detections. The idea presented in this paper intends to overcome such limitations by leveraging on the fact that the fractional double differences are characterized by having a piecewise linear trend, with different slopes and intercepts. By evaluating the dispersion of such two parameters instead of the double difference measurements directly, it is possible to design a more robust spoofing detector. The performance of this linear regression-based method is very promising, since no cases of false alarms or of missed detections have been observed in all the performed tests.

There are efforts worldwide to build and launch new Low Earth Orbit (LEO) satellites for a multitude of communication and remote-sensing applications. The high number of LEO satellites soon to be available, their relative proximity to Earth compared to GNSS satellites, as well as the potential of Doppler-based positioning makes these LEO systems good candidates for future positioning solutions, to complement the existing GNSS and terrestrial navigation. LEO systems for Positioning, Navigation, and Time (PNT), briefly referred to as LEO-PNT, can be built either by reusing the existing constellations as signals of opportunity (SoO), or by building new LEO constellations optimized for the positioning purpose. The goal of this paper is to offer a comprehensive comparison in terms of code-based and Doppler-based Geometric Dilution of Precision (GDOP) between existing LEO systems and to discuss the optimization steps to follow in building novel LEO-PNT constellations for best positioning performance. We show that existing broadband LEO constellation with thousands or more satellites are good candidates for SoO in positioning and they can offer close to 100% coverage. acceptedVersion

Nowadays, the Global Navigation Satellite Systems (GNSS) technology is not the primary means of navigation for civil aviation and Air Traffic Control, but its role is increasing. Consequently, the vulnerabilities of GNSSs to Radio Frequency Interference, including the dangerous intentional sources of interference (i.e., jamming and spoofing), raise concerns and special attention also in the aviation field. This panorama urges for figuring out effective solutions able to cope with GNSS interference and preserve safety of operations. In the frame of a Single European Sky Air traffic management Research (SESAR) Exploratory Research initiative, a novel, effective, and affordable concept of GNSS interference management for civil aviation has been developed. This new interference management concept is able to raise early warnings to the on-board navigation system about the detection of interfering signals and their classification, and then to estimate the Direction of Arrival (DoA) of the source of interference allowing the adoption of appropriate countermeasures against the individuated source. This paper describes the interference management concept and presents the on-field tests which allowed for assessing the reached level of performance and confirmed the applicability of this approach to the aviation applications. publishedVersion

During the last decade, the number of applications for land transportation that depend on systems for accurate positioning has significantly increased. Unfortunately, systems based on low-cost global navigation satellite system (GNSS) components harshly suffer signal impairments due to the environment surrounding the antenna, but new designs based on deeper data fusion and on the combination of different signal processing techniques can overcome limitations without the introduction of expensive components. Supported by a complete mathematical model, this paper presents the design of a real-time positioning system that is based on the tight integration of extremely low-cost sensors and a consumer-grade global positioning system receiver. The design has been validated experimentally through a series of tests carried out in real scenarios. The performance of the new system is compared against a standalone GNSS receiver and survey-grade professional equipment. The results show that a carefully designed and constrained integration of low-cost sensors can have performance comparable to that of an expensive professional equipment.

The purpose of this paper is to present and discuss the results of a Spoofing Detection and Direction Finding procedure usable for GNSS spoofing monitoring on board of a commercial aircraft. The targeted use case is an evolution of the GNSS equipment used on board of civil aircrafts, characterized by the availability of more GNSS constellations (multi-constellation) and two carrier frequencies (dual-frequency). The GNSS evolution in the civil aviation domain also foresees the progressive acquisition of a prominent role for GNSS in the aircraft navigation system, while today this role is taken by the traditional terrestrial NavAids. In such a scenario, the safe management of the Radio Frequency (RF) interference risk on GNSS becomes a primary task; nonetheless, the strict regulatory and certification procedures of the civil aviation domain impose a smooth technological evolution for the onboard avionic systems and consequent constraints in the design of new functionalities. The Spoofing Detection and Direction Finding procedure presented in this paper has been developed in this context. It consists of a detection module that employs the Dispersion of Double Difference (D3 ) algorithm to identify which signals tracked by the receiver are counterfeit, if any; the detection module is followed by a direction finding module that implements an efficient Direction-OfArrival estimator, with the purpose of providing information to a ground control center for the localization of the spoofing source (this ground segment is out of the scope of the paper). The necessary on board equipment consists in three GNSS antennas and the same number of receivers, time-synchronized with a common clock, plus a signal processor that implements the detection and DOA estimation algorithms. The paper presents the design of the chain of algorithms and their preliminary tests in a laboratory setup, with the simulation of a number of spoofing attacks, assumed realistic in a civil aviation scenario. The obtained results confirm the reliability of the spoofing detection and direction finding procedure, in terms of both correct detection rate and DOA estimate accuracy. They also show that the quality of the carrier phase measurements is a key element of the whole procedure, therefore an interesting indication we obtain is that the choice of the on-board multi-constellation GNSS receivers should take into account also the accuracy and continuity of the provided carrier phase measurements.

This paper proposes low-complexity measures to be deployed on most aircraft to enable the management of Global Navigation Satellite Systems (GNSS) interference, and in particularly of jamming and spoofing threats, in order to reach the Flightpath2050 safety and security targets. It is known that, if there is a jamming interference and GNSS navigation is lost, a disruption will be caused, requiring the likely intervention of Air Traffic Control. Also, the presence of a spoofing signal is a serious security threat with a potentially catastrophic impact on the safety of the aircraft and on any other ground infrastructure. Through our jamming and spoofing detection and localization stages, based on minimal additional infrastructure, we will reduce the time required to detect and localize an interfering source and, therefore, the time required to mitigate it and restore the nominal traffic operations. Our solutions will also improve the safety of the Air Traffic Management system. Moreover, the deployment of our solution, with its capability of localizing an interfering device, could be a deterrent to any agent interested in intentionally generating a jamming or spoofing signal, and so will reduce the likelihood of this type of interference events.&nbsp

In the last decades, several positioning and navigation algorithms have been developed to enhance vehicular localization capabilities. Thanks to ad-hoc communication networks, the exchange of navigation data and positioning solutions has been exploited to the purpose. This trend has recently suggested the extension of state-of-the art navigation algorithms to the hybridization of independent heterogeneous measurements within collaborative frameworks. In this paper an integration paradigm based on the combination of Global Navigation Satellite System (GNSS) observable measurements is analysed. In this work, a comparison among legacy Extended Kalman Filter (EKF) and a suboptimal Particle Filter (s-PF) is proposed. First we show that under the same assumptions in non-collaborative framework the s-PF easily overcome EKF performances at the cost of a higher computational cost. On the contrary, by analysing a realistic scenario in which a target agent is aided by a set of collaborating peers we showed that a hybridized EKF implementation allows reaching and overcome PF performance at the only expense of network connectivity among few GNSS receivers, while the proposed integration induces minor benefits for an efficient s-PF.

Even though state-of-the-art RTK-based GNSS receivers usually match the demanding requirements posed by precise agriculture (PA) applications in open sky conditions, propagation effects degrade the performance under foliage or in presence of surrounding obstacles. This paper introduces a hybrid GNSS/INS system able to overcome some of the current limitations of GNSS in constrained agriculture environments. Authors performed several tests in different agricultural scenarios, such as vineyards, orchards and greenhouses. The paper presents the performance assessment through the evaluation of specific Key Performance Indicators (KPIs) and the benchmarking of this system with state-of-the art receivers, evaluating the benefits provided by new GNSS constellations and signals.

With the advent of new services and the evolution of transportation systems the need of a reliable and accurate knowledge of the position information became of paramount importance. Among the multiple technologies and sensors that might be used for positioning, Global Navigation Satellite System (GNSS) can be considered as the core technology and a superior system capable to provide the user with estimates of his own position in a global reference frame. Unfortunately, received GNSS signals are extremely weak and thus vulnerable to non-intentional and intentional Radio Frequency Interference. In particular, the malicious intentional interference produced by jammers is proliferating and it is becoming a serious threat for GNSS receivers. The integration of GNSS and external sensors with complementary characteristics, is the key for overcoming the weaknesses and enhancing the strengths of each sub-system. In this paper, the design of an ultra-tight hybrid navigation system is proposed with the aim to enhance the robustness of the navigation system in presence of jamming. A GNSS unit has been integrated with external sensors, namely Inertial Navigation Systems, visual sensor and odometer. Exploiting the “record and replay” of real GNSS signal artificially augmented with jamming interference within the L1 GNSS bandwidth, the performance has been assessed in controlled scenarios. The results presented in this paper show that the multi-sensor integrated system can sustain the navigation even in presence of severe jamming, although such a strong interference completely masks the GNSS signal denying the navigation of a GNSS stand-alone system. Moreover, the test procedures suitability of the record and replay approach for the jamming scenario generation and combination with real datasets are discussed and analyzed.

In modern GNSS receivers, using a Kalman filter in each signal tracking loop presents remarkable advantages in terms of accuracy and robustness against malicious noise sources, but poses critical issues in real-time applications due to the high computational cost. For this reason, we propose an efficient method to dramatically reduce the number of operations involved in the execution of the Kalman filter. In particular, the relationship between Kalman gain and noise covariances is analyzed in detail, showing that the gain computation can be greatly simplified by using an appropriately calibrated, small-size lookup table (LUT). The loss of performance of the proposed method, due to its inherent approximations, is shown to be negligible with respect to a traditional implementation upon careful system model and LUT calibration. Furthermore, a very fast signal tracking recovery after an outage is guaranteed. The implementation complexity of the simplified LUT-based method is compared with traditional approaches, proving that the proposed method has a tremendous advantage in terms of computational and storage requirements.

This paper presents the development of a dual-antenna GNSS spoofing detection technique based on the analysis of the dispersion of the double differences of carrier phase measurements produced by two GNSS receivers. No synchronization of the receivers is needed for the algorithm to properly work. The algorithm is derived from the idea of the Sum of Squares (SoS) detector, recently presented as a simple and efficient way to detect a common angle of arrival for all the GNSS signals arriving to a pair of antennas. The presence of such a common angle is recognized as an undiscussed indication of non-authentic GNSS signals. Nonetheless, some limitations can be identified in the SoS algorithm. First of all, the assumption that all the signals arrive from the same source; situations are possible in which the receiver tracks only a subset of counterfeit signals, out of the whole signal ensemble. The idea presented in this paper intends to overcome such limitations, properly modifying the SoS detection metric to identify subsets of counterfeit signals. The analysis is supported by several simulation tests, in both nominal and spoofed signal conditions, to prove the effectiveness of the proposed method.

The paper deals with the development of a Robust Position Unit (RPU) based on the real-time implementation of an advanced positioning algorithm. The RPU uses a tightly-coupled technique between a mass market single frequency GNSS chipset with a low cost Inertial Measurement Unit (IMU) based on Micro Electro-Mechanical Systems and an odometer. The tight integration algorithm has been obtained through the design of a complex Extended Kalman Filter (EKF). Its performance has been verified running the designed real-time algorithm in different challenging environments: an urban scenario characterized by narrow streets, few satellites in view and tree-lined avenues. A second harsh environment is represented by a mountain area where the vehicle has driven through long tunnels, overpasses and sharp road bends. The tests showed how a tight integration algorithm, designed by using raw data from only low-cost sensors, can provide real advantages at a price of careful customizations and adaptations that take into account the particular use and environment.

In the modern automotive industry, the wide variety of Intelligent Transport System (ITS) services needs a positioning unit. The core of such unit is a Global Navigation Satellite System (GNSS) receiver hybridized or assisted by other sensors or data. In harsh environments, such as urban scenarios, the testing and performance assessment of these terminals is of paramount importance for ITS applications. In fact such environments are very challenging for the GNSS core unit. This paper proposes some test procedures suitable to assess the performance of GNSS receivers, as part of an hybridyzed positioning unit, in urban scenarios. The benefits of a GNSS receiver based on the Software Defined Radio (SDR) technology is exploited by presenting a set of results obtained on field tests.

Global Navigation Satellite Systems (GNSSs) remain the principal mean of positioning in many applications and systems, but in several types of environment, the performance of standalone receivers is degraded. Although many works show the benefits of the integration between GNSS and Inertial Navigation Systems (INSs), tightly-coupled architectures are mainly implemented in professional devices and are based on high-grade Inertial Measurement Units (IMUs). This paper investigates the performance improvements enabled by the tight integration, using low-cost sensors and a mass-market GNSS receiver. Performance is assessed through a series of tests carried out in real urban scenarios and is compared against commercial modules, operating in standalone mode or featuring loosely-coupled integrations. The paper describes the developed tight-integration algorithms with a terse mathematical model and assesses their efficacy from a practical perspective.

Since the setup of GNSS (Global Navigation Satellite System), receiver technique has been developing all this long way. Traditionally, A conventional system-level tacking loop normally consists of a FLL, a PLL, a DLL which are separately in charge of the continuous estimations of Doppler frequency, carrier phase, and code phase. All these three componential loops are derived from one common control system and work in an independent manner which may sacrifice the accuracy and dynamic capability. In this paper, with the background of power-saving receiver design, a new advanced tracking loop derived from the Kalman-filter-based tracking loop is proposed. In contrast to the currently available counterpart strategies, the advantages of the new strategy are discussed and analyzed, furthermore, one typical campaign is executed to prove the outstanding performance of the new power-saving tracking loop. Finally, the future work and conclusions are presented.

The availability and accuracy constraints that must be satisfied by GNSS-based positioning systems make the Kalman filter solution a key point in order to reach the required level of performance. For example, the update process of the filter requires a careful weighting of the GNSS measurements to work properly. This paper presents a method that initializes the weight of the GNSS measurements taking into account also the geometry of the constellation used for the states computation. Since the proposed method involves only the measurements covariance matrix related to the measurements, it is independent from the type of application and the number of states used within the Kalman filter. In this paper, for example, the method has been successfully applied to a GPS/IMU tight integration and then it has been compared to other approaches described in the scientific literature.

New GNSS tracking architectures are becoming of scientific interest since the last decade. The traditional DLL/PLL tracking-loops have been progressively substituted by KF/EKF scalar tracking and VDLL/VDFLL vector tracking methods. In this paper we will analyze in depth the performance of a KF scalar tracking and VDFLL vector in case of signal outage. Differently from papers on the same topic, we will show how the performance of a KF, when used within the tracking stage during a signal's blockage, are strongly dependent on a proper setting of the noise covariance matrices Q and P of the system, otherwise cases of frequency false-lock can occur when the outage condition lasts for a long time. Conversely, a VDFLL vector tracking algorithm is able to provide more precise frequency and code-phase's estimations then the KF scalar in case of signal's unavailability, but its performance relies on the navigation solution. Therefore, the effects of the PVT accuracy on the estimation of the main tracking parameters are also presented in this paper.

This paper presents the design of a truly low-cost multi-GNSS receivers /IMU navigation system that exploits the benefits of GNSS technology in attitude determination. The sensor package of the system consists of 4 single chipset receivers, 4 antennas, and one low grade MEMS IMU. Both parts, the GNSS and the IMU ones, are integrated by following a heading-constrained Tightly-Coupled approach, where inertial and GNSS raw measurements as well as GNSS computed heading are blended into an Extended Kalman Filter. To compute the attitude solution based on GNSS carrier phase measurements an integer ambiguity resolution algorithm has been developed, which takes advantage of the a priori solution of the EKF to reduce the search space and to provide an on-the-fly solution. The heading-constrained Tightly-Coupled approach followed in this paper has shown superior attitude accuracies when compared to the regular Tightly-Coupled implementation.

As more and more attention is received by the underlying passive potential of Global Navigation Satellite Systems (GNSS) signals, reflectometry and scatterometry are visited in this paper in order to assess water surface identification and aircraft altimetry performance. This is possible thanks to the high reflectivity of Global Positioning System (GPS) signals in the L-Band frequency range on water as well as ice and snow covered surfaces which compensates for the otherwise low signal intensity. Indeed, GNSS reflectometry belongs to the class of bistatic radar systems where transmitter and receiver reside at different locations and signals pass through a reflecting surface. Code phase and carrier phase based time delay measurements between the direct path and reflected signals, are used to determine the small aircraft's height with respect to the reflecting surface. On the other hand, specular reflection points are computed, and a comparison of the reflected power to the direct received power is shown to yield a good water surface identification performance. Code and carrier phase measurements are implemented in this paper to obtain cm level height estimates.

On the 20th of October 2011, the first two satellites of the Galileo constellation have been launched successfully. The possibility to have these two satellites in orbit represented a great opportunity for researchers to receive and monitor the first Galileo signals. After the satellite switch-on, we continuously monitored the signal broadcast by both the satellites, using a software radio receiver. This paper shows the analysis on the navigation message that was broadcast by the IOV-PFM Galileo satellite on the E1 bandwidth, starting from December 2011. In particular, we were able to observe a valid ephemeris data on December 21st and December 22nd. The successful interpretation of the navigation message enabled the first GPS+Galileo experimentations using real signals that were performed in static condition, through code-phase measurements.

A Global Navigation Satellite System (GNSS) receiver is able to compute the user position through a trilateration procedure, which includes the measure of the distance between the receiver and a set of satellites. Two different approaches are tipically used and implemented in commercial receivers. The former relies on code tracking, the latter leverages carrier phase measurements performed during carrier tracking.

The European Rail Traffic Management System/European Train Control System (ERTMS/ETCS) has been developed by the European Union to enhance safety and efficiency of the railway transportation. So far, the positioning and monitoring of the trains have been based mainly on trackside equipment named balises: the introduction of Global Navigation Satellite Systems (GNSS) to implement an absolute positioning system on trains could lead to a better utilization of the line with a consequent line capacity increase, along with reliability increasing and cost reducing due to the removal of expensive infrastructures. This paper describes the insertion of a state-of-the-art channel model, named Land Mobile Satellite Channel Model (LMSCM) properly adapted to represent railways scenarios, into a hardware-in-the-loop system, where the parameters generated by LMSCM are used to configure an RF GNSS signal generator. The use of the hardware generator eases the simulation of the entire GNSS constellation, while the availability of the RF signal enables the assessment of the effects of rail-domain multipath on commercial receivers. The resulting model, named Rail-Land Mobile Satellite Channel Model (Rail-LMSCM), has been enhanced with the support of different kind of scenarios a train may encounter around the rail track, as well as with the introduction of GNSS outages due to the presence of tunnels. Such enhancements make the Rail-LMSCM a more realistic average representation of the railway environment. The implemented Rail-LMSCM has been used to test in the lab the local propagation effects on the performance of a complete GNSS receiver.