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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.

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.

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.

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

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.

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., Support funded by the Spanish Ministry of Economy and Competitiveness under DELPHIS project: TEC 2009-09712.

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.

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.

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.

In recent years, Kalman filter (KF)-based tracking loop architectures have gained much attention in the Global Navigation Satellite System field and have been widely investigated due to its robust and better performance compared with traditional architectures. However, less attention has been paid to the in-depth theoretical analysis of the tracking structure and to the effects of Kalman tuning. A new approach is proposed to analyze the KF-based tracking loop. A control system model is derived according to the mathematical expression of the Kalman system. Based on this model, the influence of the choice of the setting parameters on the temporal evolution of the system response is discussed from the perspective of a control system. As a result, a reasoned and complete suite of criteria to tune the initial error covariance as well as the process and measurements noise covariances is demonstrated. Furthermore, a strategy is presented to make the system more robust in higher order dynamics without degrading the accuracy of carrier phase and Doppler frequency estimates.

In this paper, the structure of a tracking loop with Extended Kalman Filter (EKF) is analyzed. Particular emphasis is given to the NCO update rule, which is seldom mentioned or studied in previous literature. Furthermore, the structure of an EKF-based software receiver is proposed including the special modules dedicated to the initialization and maintenance of the tracking loop. The EKF-based tracking architecture has been compared with a traditional one based on an FLL/PLL+DLL architecture, and the benefit of the EKF within the tracking stage has been evaluated in terms of final positioning accuracy. Further tests have been carried out to compare the Position-Velocity-Time (PVT) solution of this receiver with the one provided by two commercial receivers: a mass-market GPS module (Ublox LEA-5T) and a professional one (Septentrio PolaRx2e@). The results show that the accuracy in PVT of the software receiver can be remarkably improved if the tracking is designed with a proper EKF architecture and the performance we can achieve is even better than the one obtained by the mass market receiver, even when a simple one-shot least-squares approach is adopted for the computation of the navigation solution.

This paper considers estimation problems where inequality constraints are imposed on the outputs of linear systems and can be modeled by nonlinear functions. In this case, censoring functions can be designed to constrain measurements for use by filters and smoothers. It is established that the filter and smoother output estimates are unbiased, provided that the underlying probability density functions are even and the censoring functions are odd. The Bounded Real Lemma is employed to ensure that the output estimates satisfy a performance criterion. A global positioning system (GPS) and inertial navigation system (INS) integration application is discussed in which a developed solution exhibits improved performance during GPS outages when a priori information is used to constrain the altitude and velocity measurements.