Signal processing techniques for GNSS anti-spoofing algorithms

The Global Navigation Satellite Systems (GNSS) usage is growing at a very high rate, and more applications are relying on GNSS for correct functioning. With the introduction of new GNSSs, like the European Galileo and the Chinese Beidou, in addition to the existing ones, the United States Global Positioning System (GPS) and the Russian GLONASS, the applications, accuracy of the position and usage of the signals are increasing by the day. Given that GNSS signals are received with very low power, they are prone to interference events that may reduce the usage or decrease the accuracy. From these interference, the spoofing attack is the one that has drawn major concerns in the GNSS community. A spoofing attack consist on the transmission of GNSS-like signals, with the goal of taking control of the receiver and make it compute an erroneous position and time solution. In the thesis, we focus on the design and validation of different signal processing techniques, that aim at detection and mitigation of the spoofing attack effects. These are standalone techniques, working at the receiver’s level and providing discrimination of spoofing events without the need of external hardware or communication links. Four different techniques are explored, each of them with its unique sets of advantages and disadvantages, and a unique approach to spoofing detection. For these techniques, a spoofing detection algorithm is designed and implemented, and its capabilities are validated by means of a set of datasets containing spoofing signals. The thesis focuses on two different aspects of the techniques, divided as per detection and mitigation capabilities. Both detection techniques are complementary, their joint use is explored and experimental results are shown that demonstrate the advantages. In addition, each mitigation technique is analyzed separately as they require specialized receiver architecture in order to achieve spoofing detection and mitigation. These techniques are able to decrease the effects of the spoofing attacks, to the point of removing the spoofing signal from the receiver and compute navigation solutions that are not controlled by the spoofer and lead in more accurate end results. The main contributions of this thesis are: the description of a multidimensional ratio metric test for distinction between spoofing and multipath effects; the introduction of a cross-check between automatic gain control measurements and the carrier to noise density ratio, for distinction between spoofing attacks and other interference events; the description of a novel signal processing method for detection and mitigation of spoofing effects, based on the use of linear regression algorithms; and the description of a spoofing detection algorithm based on a feedback tracking architecture

Security Evaluation of GNSS Signal Quality Monitoring Techniques against Optimal Spoofing Attacks

GNSSs have a significant impact on everyday life and, therefore, the are increasingly becoming an attractive target for illicit exploitation. As such, anti-spoofing algorithms have become an relevant research topic within the GNSS discipline. This Thesis provides a review of recent research in the field of GNSS spoofing/anti-spoofing, designs a method to generate an energy optimal spoofing signal and evaluates the performance of the anti-spoofing signal quality monitoring techniques against it

GNSS Vulnerabilities and Existing Solutions:A Review of the Literature

This literature review paper focuses on existing vulnerabilities associated with global navigation satellite systems (GNSSs). With respect to the civilian/non encrypted GNSSs, they are employed for proving positioning, navigation and timing (PNT) solutions across a wide range of industries. Some of these include electric power grids, stock exchange systems, cellular communications, agriculture, unmanned aerial systems and intelligent transportation systems. In this survey paper, physical degradations, existing threats and solutions adopted in academia and industry are presented. In regards to GNSS threats, jamming and spoofing attacks as well as detection techniques adopted in the literature are surveyed and summarized. Also discussed are multipath propagation in GNSS and non line-of-sight (NLoS) detection techniques. The review also identifies and discusses open research areas and techniques which can be investigated for the purpose of enhancing the robustness of GNSS

GNSS interference management techniques against malicious attacks

This thesis collects the outcomes of a Ph.D. course in Telecommunications Engineering and it is focused on the study and design of possible techniques able to counteract interference signal in Global Navigation Satellite System (GNSS) systems. The subject is the jamming threat in navigation systems, that has become a very increasingly important topic in recent years, due to the wide diffusion of GNSS-based civil applications. Detection and mitigation techniques are developed in order to fight out jamming signals, tested in different scenarios and including sophisticated signals. The thesis is organized in two main parts, which deal with management of GNSS intentional counterfeit signals. The first part deals with the interference management, focusing on the intentional interfering signal. In particular, a technique for the detection and localization of the interfering signal level in the GNSS bands in frequency domain has been proposed. In addition, an effective mitigation technique which exploits the periodic characteristics of the common jamming signals reducing interfering effects at the receiver side has been introduced. Moreover, this technique has been also tested in a different and more complicated scenario resulting still effective in mitigation and cancellation of the interfering signal, without high complexity. The second part still deals with the problem of interference management, but regarding with more sophisticated signal. The attention is focused on the detection of spoofing signal, which is the most complex among the jamming signal types. Due to this highly difficulty in detect and mitigate this kind of signal, spoofing threat is considered the most dangerous. In this work, a possible techniques able to detect this sophisticated signal has been proposed, observing and exploiting jointly the outputs of several operational block measurements of the GNSS receiver operating chain

GNSS Integrity Monitoring assisted by Signal Processing techniques in Harsh Environments

The Global Navigation Satellite Systems (GNSS) applications are growing and more pervasive in the modern society. The presence of multi-constellation GNSS receivers able to use signals coming from different systems like the american Global Positioning System (GPS), the european Galileo, the Chinese Beidou and the russian GLONASS, permits to have more accuracy in position solution. All the receivers provide always more reliable solution but it is important to monitor the possible presence of problems in the position computation. These problems could be caused by the presence of impairments given by unintentional sources like multipath generated by the environment or intentional sources like spoofing attacks. In this thesis we focus on design algorithms at signal processing level used to assist Integrity operations in terms of Fault Detection and Exclusion (FDE). These are standalone algorithms all implemented in a software receiver without using external information. The first step was the creation of a detector for correlation distortion due to the multipath with his limitations. Once the detection is performed a quality index for the signal is computed and a decision about the exclusion of a specific Satellite Vehicle (SV) is taken. The exclusion could be not feasible so an alternative approach could be the inflation of the variance of the error models used in the position computation. The quality signal can be even used for spoofinng applications and a novel mitigation technique is developed and presented. In addition, the mitigation of the multipath can be reached at pseudoranges level by using new method to compute the position solution. The main contributions of this thesis are: the development of a multipath, or more in general, impairments detector at signal processing level; the creation of an index to measure the quality of a signal based on the detector’s output; the description of a novel signal processing method for detection and mitigation of spoofing effects, based on the use of linear regression algorithms; An alternative method to compute the Position Velocity and Time (PVT) solution by using different well known algorithms in order to mitigate the effects of the multipath on the position domain

PNT cyber resilience : a Lab2Live observer based approach, Report 1 : GNSS resilience and identified vulnerabilities. Technical Report 1

The use of global navigation satellite systems (GNSS) such as GPS and Galileo are vital sources of positioning, navigation and timing (PNT) information for vehicles. This information is of critical importance for connected autonomous vehicles (CAVs) due to their dependence on this information for localisation, route planning and situational awareness. A downside to solely relying on GNSS for PNT is that the signal strength arriving from navigation satellites in space is weak and currently there is no authentication included in the civilian GNSS adopted in the automotive industry. This means that cyber-attacks against the GNSS signal via jamming or spoofing are attractive to adversaries due to the potentially high impact they can achieve. This report reviews the vulnerabilities of GNSS services for CAVs (a summary is shown in Figure 1), as well as detection and mitigating techniques, summarises the opinions on PNT cyber testing sourced from a select group of experts, and finishes with a description of the associated lab-based and real-world feasibility study and proposed research methodology

Performance Comparison Of Weak And Strong Learners In Detecting GPS Spoofing Attacks On Unmanned Aerial Vehicles (uavs)

Unmanned Aerial Vehicle systems (UAVs) are widely used in civil and military applications. These systems rely on trustworthy connections with various nodes in their network to conduct their safe operations and return-to-home. These entities consist of other aircrafts, ground control facilities, air traffic control facilities, and satellite navigation systems. Global positioning systems (GPS) play a significant role in UAV\u27s communication with different nodes, navigation, and positioning tasks. However, due to the unencrypted nature of the GPS signals, these vehicles are prone to several cyberattacks, including GPS meaconing, GPS spoofing, and jamming. Therefore, this thesis aims at conducting a detailed comparison of two widely used machine learning techniques, namely weak and strong learners, to investigate their performance in detecting GPS spoofing attacks that target UAVs. Real data are used to generate training datasets and test the effectiveness of machine learning techniques. Various features are derived from this data. To evaluate the performance of the models, seven different evaluation metrics, including accuracy, probabilities of detection and misdetection, probability of false alarm, processing time, prediction time per sample, and memory size, are implemented. The results show that both types of machine learning algorithms provide high detection and low false alarm probabilities. In addition, despite being structurally weaker than strong learners, weak learner classifiers also, achieve a good detection rate. However, the strong learners slightly outperform the weak learner classifiers in terms of multiple evaluation metrics, including accuracy, probabilities of misdetection and false alarm, while weak learner classifiers outperform in terms of time performance metrics

A Review of Selected Applications of GNSS CORS and Related Experiences at the University of Palermo (Italy)

Services from the Continuously Operating Reference Stations (CORS) of the Global Navigation Satellite System (GNSS) provide data and insights to a range of research areas such as physical sciences, engineering, earth and planetary sciences, computer science, and environmental science. Even though these fields are varied, they are all linked through the GNSS operational application. GNSS CORS have historically been deployed for three-dimensional positioning but also for the establishment of local and global reference systems and the measurement of ionospheric and tropospheric errors. In addition to these studies, CORS is uncovering new, emerging scientific applications. These include real-time monitoring of land subsidence via network real-time kinematics (NRTK) or precise point positioning (PPP), structural health monitoring (SHM), earthquake and volcanology monitoring, GNSS reflectometry (GNSS-R) for mapping soil moisture content, precision farming with affordable receivers, and zenith total delay to aid hydrology and meteorology. The flexibility of CORS infrastructure and services has paved the way for new research areas. The aim of this study is to present a curated selection of scientific papers on prevalent topics such as network monitoring, reference frames, and structure monitoring (like dams), along with an evaluation of CORS performance. Concurrently, it reports on the scientific endeavours undertaken by the Geomatics Research Group at the University of Palermo in the realm of GNSS CORS over the past 15 years

Multipath Propagation, Mitigation and Monitoring in the Light of Galileo and the Modernized GPS

Among the numerous potential sources of GNSS signal degradation, multipath takes on a prominent position. Unlike other errors like ionospheric or tropospheric path delays which can be modeled or significantly reduced by differential techniques, multipath influences cannot be mitigated by such approaches. Although a lot of multipath mitigation techniques have been proposed and developed in the past among them many receiver internal approaches using special signal processing algorithms multipath (especially multipath with small geometric path delays) still remains a major error source. This is why multipath has been a major design driver for the definition of the Galileo signal structure carried out in the past years and the subsequent signal optimization activities. This thesis tries to provide a broad and comprehensive insight into various aspects of multipath propagation, mitigation and monitoring (without claiming to be exhaustive). It contains an overview of the most important aspects of multipath propagation, including the discussion of different types of multipath signals (e.g. specular vs. diffuse multipath, satellite vs. receiver multipath or hardware-induced multipath), typical characteristics such as periodic signal variations whose frequency depends on the satellite-antenna-reflector geometry and the impact on the signal tracking process within a GNSS receiver. A large part of this thesis is dedicated to aspects of multipath mitigation, first providing a summary of the most common multipath mitigation techniques with a special focus on receiver-internal approaches such as the narrow correlation technique, double-delta correlator implementations, the Early-Late Slope (ELS) technique or Early/Early tracking implementations. However, other mitigation approaches such as using arrays of closely spaced antennas or multipath-limiting antennas are discussed as well. Some of these techniques are used for subsequent multipath performance analyses considering signals of the (modernized) GPS and Galileo. These analyses base on a new methodology to estimate typical and meaningful multipath errors making use of multipath error envelopes that are scaled in a suitable way to account for different multipath environments. It will be shown that typical (mean) multipath errors can be derived from these scaled envelopes by computation of the envelopes running average and that these mean multipath errors are of the same order as multipath errors obtained from complex statistical channel models. Another part of this thesis covers various aspects of multipath detection and monitoring. First, current techniques for multipath detection and monitoring are described and discussed with respect to their benefits and drawbacks or their real-time capability. Among the considered approaches are techniques like code minus carrier monitoring, SNR monitoring, the use of differenced observations or spectral and wavelet analysis. Following this introductory overview, a completely new approach for real-time multipath monitoring by processing multi-correlator observations will be introduced. Previously being used primarily for the detection of Evil Waveforms (signal failures that originate from a malfunction of the satellites signal generation and transmission hardware), the same basic observations (linear combinations of correlator outputs) can be used for the development of a multi-correlator-based real-time multipath monitoring system. The objective is to provide the user with instant information whether or not a signal is affected by multipath. The proposed monitoring scheme has been implemented in the form of a Matlab-based software called RTMM (Real-Time Multipath Monitor) which has been used to verify the monitoring approach and to determine its sensitivity.Die Qualität eines Satellitensignals wird durch eine Vielzahl potenzieller Fehlerquellen negativ beeinflusst. Neben atmosphärischen Einflüssen tragen Mehrwegeeinflüsse einen wesentlichen Anteil zum Gesamtfehlerbudget der Satellitennavigation bei. Während eine ganze Reihe von Fehlereinflüssen durch geeignete Modellierung oder differenzielle Verfahren deutlich reduziert werden können, ist dies durch die räumliche Dekorrelation der Mehrwegeeffekte nicht möglich. Obwohl in der Vergangenheit eine Vielzahl von Verfahren zur Mehrwegereduzierung vorgeschlagen und entwickelt wurden, stellen Mehrwegesignale noch immer eine wesentliche, stets zu berücksichtigende Fehlerquelle dar. Aus diesem Grund spielten die zu erwartenden Mehrwegefehler auch eine sehr wichtige Rolle im Zuge der Definition sowie der Optimierung der Galileo-Signalstruktur und können somit als wesentliches Design-Kriterium angesehen werden. Die vorliegende Arbeit gibt einen umfassenden Einblick in verschiedene Aspekte der Mehrwegeausbreitung, -reduzierung sowie der Detektion und der Überwachung auftretender Mehrwegeeffekte. Die Arbeit beschreibt zunächst die wichtigsten Aspekte der Mehrwegeausbreitung, wobei beispielsweise unterschiedliche Arten von Reflexionen oder unterschiedliche Entstehungsarten ebenso diskutiert werden wie typische Auswirkungen von Mehrwegesignalen wie die Entstehung periodischer Signalvariationen. Solche Signalvariationen sind in starkem Maße abhängig von der durch die Satellitenposition, dem Antennenstandpunkt und der Lage des Reflexionspunktes definierten Geometrie. Die Frequenz dieser Signalvariationen wird für unterschiedliche geometrische Verhältnisse berechnet. Zudem werden der Einfluss bzw. die Auswirkungen einer Mehrwegeausbreitung auf den Signalverarbeitungsprozess in einem GNSS Empfänger aufgezeigt. Einen weiteren Schwerpunkt dieser Arbeit bilden die derzeit gebräuchlichen Methoden zur Reduzierung von Mehrwegeeinflüssen. Dabei werden zunächst die wichtigsten empfängerinternen Ansätze vorgestellt. Aber auch Methoden wie die Verwendung von Antennenarrays oder spezieller Antennen bleiben nicht unberücksichtigt. Einige dieser Methoden bilden im Folgenden die Grundlage für die Bestimmung von typischen Mehrwegefehlern. Dazu wird eine neuartige Methodik vorgestellt, um aus Hüllkurven des Mehrwegefehlers aussagekräftige mittlere Mehrwegefehler zu bestimmen. Hierzu werden die Hüllkurven mit Hilfe einiger aus statistischen Kanalmodellen abgeleiteter Parameter in geeigneter Weise skaliert, um unterschiedlichen Mehrwegeumgebungen Rechnung zu tragen. Es wird gezeigt, dass die mit Hilfe dieser relativ einfachen und effizienten Methode ermittelten Mehrwegefehler in derselben Größenordnung liegen wie die aus komplexen statistischen Kanalmodellen ermittelten Fehler. Einen weiteren Themenkomplex stellen Methoden zur Detektion und zum Monitoring von Mehrwegeeinflüssen dar. Dabei werden zunächst derzeit verwendete Ansätze vorgestellt und hinsichtlich ihrer Vor- und Nachteile sowie hinsichtlich ihrer Echtzeitfähigkeit diskutiert. In Anschluss daran wird ein neuartiger Ansatz zur Detektion und zum Monitoring von Mehrwegesignalen in Echtzeit vorgestellt, der auf der Auswertung von Multikorrelatorbeobachtungen basiert. Ziel dieser Entwicklung ist es, einen potenziellen Nutzer sofort darüber informieren zu können, wenn ein Signal mit Mehrwegefehlern behaftet ist. Der vorgeschlagene Ansatz wurde in Form einer Matlab-basierten implementiert, welche im Folgenden zur Verifizierung und zur Bestimmung der Empfindlichkeit des Verfahrens verwendet wird

Interference Management and System Optimization with GNSS and non-GNSS Signals for Enhanced Navigation

In the last few decades, Global Navigation Satellite System (GNSS) has become an indispensable element in our society. Currently, GNSS is used in a wide variety of sectors and situations, some of them offering critical services, such as transportation, telecommunications, and finances. For this reason, and combined with the relative ease an attack on the GNSS wireless signals can be performed nowadays with an Software Defined Radio (SDR) transmitter, GNSS has become more and more a target of wireless attacks of diverse nature and motivations. Nowadays, anyone can buy an interference device (also known as a jammer device) for a few euros. These devices are legal to be bought in many countries, especially online. But at the same time, they are illegal to be used. These devices can interfere with signals in specific frequency bands, used for services such as GNSS. An outage in the GNSS service at a specific location area (which can be even a few km2) could end up in disastrous consequences, such as an economical loss or even putting lives at risk, since many critical services rely on GNSS for their correct functioning. Fundamentally, this thesis focuses on developing new methods and algorithms for interference management in GNSS. The main focus is on interference detection and classification, but discussions are also made about interference localization and mitigation. The detection and classification algorithms analyzed in this thesis are chosen from the point of view of the aviation domain, in which additional constraints (e.g., antenna placement, number of antennas, vibrations due to movement, etc.) need to be taken into account. The selected detection and classification methods are applied at the pre-correlation level, based on the raw received signal. They apply specific signal transforms in the digital domain (e.g., time-frequency transformations) to the received signal. With such algorithms, interferences can be detected at a level as low as 0 dB Jamming-to-Signal Ratio (JSR). The interference classification combines transformed signals with previously trained signals Convolutional Neural Network (CNN) and/or Support Vector Machine (SVM) to determine the type of interference signal among the studied ones. The accuracy of such a classification methodology is above 90%. Knowing which signal causes interference we can better optimize which mitigation and localization algorithm we should use to obtain the best mitigation results. Furthermore, this thesis also studies alternative positioning methods, starting from the premise that GNSS may not always be available and/or we are certain that we can not rely on it due to some reason such as high or unmitigated interferences. Therefore, if one needs to get a Position Velocity and Time (PVT) solution, one would have to rely on alternative signals that could offer positioning features, such as the cellular network signals (i.e. 4G, 5G, and further releases) and/or satellite positioning based on Low Earth Orbit (LEO) satellites. Those systems use presumably different frequency bands, which makes it more unlikely that they will be jammed at the same time as the GNSS signal. In this sense, positioning based on LEO satellites is studied in this thesis from the point of view of feasibility and expected performance