GNSS Signal Authentication via Power and Distortion Monitoring

We propose a simple low-cost technique that enables civil Global Positioning System (GPS) receivers and other civil global navigation satellite system (GNSS) receivers to reliably detect carry-off spoofing and jamming. The technique, which we call the Power-Distortion detector, classifies received signals as interference-free, multipath-afflicted, spoofed, or jammed according to observations of received power and correlatio n function distortion. It does not depend on external hardware or a network connection and can be readily implemented on many receivers via a firmware update. Crucially, the detector can with high probability distinguish low-power spoofing from ordinary multipath. In testing against over 25 high-quality empirical data sets yielding over 900,000 separate detection tests, the detector correctly alarms on all malicious spoofing or jamming attack s while maintaining a <0.5% single-channel false alarm rate.Aerospace Engineering and Engineering Mechanic

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

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

On the use of a signal quality index applying at tracking stage level to assist the RAIM system of a GNSS receiver

In this work, a novel signal processing method is proposed to assist the Receiver Autonomous Integrity Monitoring (RAIM) module used in a receiver of Global Navigation Satellite Systems (GNSS) to improve the integrity of the estimated position. The proposed technique represents an evolution of the Multipath Distance Detector (MPDD), thanks to the introduction of a Signal Quality Index (SQI), which is both a metric able to evaluate the goodness of the signal, and a parameter used to improve the performance of the RAIM modules. Simulation results show the effectiveness of the proposed method

Performance of GNSS evil waveform detectors in the presence of multipath

The detection of evil waveforms (EWF) in GNSS signals is crucial to refrain from using anomalous signals in the PVT solution, which could degrade significantly its accuracy. The EWF detectors are, in general, based on the computation of the distortion of the code autocorrelation function. Thus, the multipath effect, an independent mechanism that also distorts the autocorrelation shape, can be incorrectly assumed by the EWF detector as the presence of EWF, leading to a major increase of the probability of false alarm. In the paper we analyze the robustness of the main EWF detectors and modulations to the presence of multipath.info:eu-repo/semantics/publishedVersio

Evaluation of GPS L5 and Galileo E1 and E5a Performance for Future Multi Frequency and Multi Constellation GBAS

In this paper, we show a performance analysis of different signals from the new Galileo satellites in the E1 and E5a frequency bands as well as GPS L5 signals in DLR’s experimental Ground Based Augmentation System (GBAS). We show results of noise and multipath evaluations of the available Galileo satellites and compare their performance to the currently used GPS L1 and the new GPS L5 signals which were presented in a recent paper. The results show that the raw noise and multipath level of Galileo signals is smaller than of GPS. Even after smoothing, Galileo signals perform somewhat better than GPS and are less sensitive to the smoothing time constant. Another issue to be considered in a future multi frequency system is inter-frequency bias. These biases differ between satellites and depend on satellite and receiver hardware, but they can be determined a priori. With known receiver and antenna configurations, it is possible to correct for these biases and avoid errors introduced by different hardware in the airborne receiver and GBAS ground system. A residual uncertainty associated with the bias correction has to be taken into account. This can be modelled as part of σ_(pr\_gnd)

Detection of GNSS Ionospheric Scintillations based on Machine Learning Decision Tree

This paper proposes a methodology for automatic, accurate and early detection of amplitude ionospheric scintillation events, based on machine learning algorithms, applied on big sets of 50 Hz post-correlation data provided by a GNSS receiver. Experimental results on real data show that this approach can considerably improve traditional methods, reaching a detection accuracy of 98%, very close to human-driven manual classification. Moreover, the detection responsiveness is enhanced, enabling early scintillation alerts

Signal quality monitoring aspects in GNSS signals affected by evil waveforms

Evil waveforms (EWF) are anomalies in the GNSS transmitted signals that can degrade significantly the accuracy of the PVT solution. The cross-correlation function of the incoming signal disturbed by EWF distortion and the locally-generated code signal is obtained analytically for threat models TM-A, TM-B and TM-C. These results are useful to evaluate efficiently the performance of EWF detectors, namely the detectability and hazard regions.info:eu-repo/semantics/publishedVersio