On the Impact of Channel Cross-Correlations in High-Sensitivity Receivers for Galileo E1 OS and GPS L1C Signals

One of the most promising features of the modernized global navigation satellite systems signals is the presence of pilot channels that, being data-transition free, allow for increasing the coherent integration time of the receivers. Generally speaking, the increased integration time allows to better average the thermal noise component, thus improving the postcorrelation SNR of the receiver in the acquisition phase. On the other hand, for a standalone receiver which is not aided or assisted, the acquisition architecture requires that only the pilot channel is processed, at least during the first steps of the procedure. The aim of this paper is to present a detailed investigation on the impact of the code cross-correlation properties in the reception of Galileo E1 Open Service and GPS L1C civil signals. Analytical and simulation results demonstrate that the S-curve of the code synchronization loop can be affected by a bias around the lock point. This effect depends on the code cross-correlation properties and on the receiver setup. Furthermore, in these cases, the sensitivity of the receiver to other error sources might increase, and the paper shows how in presence of an interfering signal the pseudorange bias can be magnified and lead to relevant performance degradatio

IMPACT OF IONOSPHERIC HORIZONTAL ASYMMETRY ON ELECTRON DENSITY PROFILES DERIVED BY GNSS RADIO OCCULTATION

The ‘Onion-peeling' algorithm is a very common technique used to invert Radio Occultation (RO) data in the ionosphere. Because of the implicit assumption of spherical symmetry for the electron density distribution in the ionosphere, the standard Onion-peeling algorithm could give erroneous concentration values in the retrieved electron density profile. In particular, this happens when strong horizontal ionospheric electron density gradients are present, like for example in the Equatorial Ionization Anomaly (EIA) region during high solar activity periods. In this work, using simulated RO TEC data computed by means of the NeQuick2 ionospheric electron density model and ideal RO geometries, we tried to formulate and evaluate an asymmetry level indicator for quasi-horizontal radio occultation observations. This asymmetry index is based on the electron density variation that a ray may experience along its propagation path (satellite to satellite link) in a RO event. Our previous qualitative assessment based on ideal simulations of RO events shows very high correlation between our asymmetry index and Onion-peeling retrieval errors (Shaikh M.M. et al 2013): errors produced by Onion-peeling in the retrieval of NmF2 and VTEC are larger at the geographical locations where our asymmetry index indicates high asymmetry in the ionosphere. In this contribution, an analysis of the asymmetry index has been carried out for the first time using real radio occultation geometries taken from COSMIC mission. This has been done for COSMIC events for which, considering the same RO geometry, simulated Limb-TEC (LTEC) under NeQuick2 background were quite close to the real LTEC observations (providing ‘quasi' co-located vertical profiles of electron density after inversion). On the basis of the outcomes of our work, for a given geometry of a real RO event and using a suitable ionospheric model, we will try to investigate the possibility to predict ionospheric asymmetry expected for the particular RO geometry considered. We could also try to evaluate, in advance, its impact on the inverted electron density profile, providing an indication of the expected product quality, if standard Onion-peelingalgorithm will be adopted to invert the observables. Results presented in this paper are initial outcomes based on our asymmetry evaluation algorith

The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines

Scintillation caused by the electron density irregularities in the ionospheric plasma leads to rapid fluctuations in the amplitude and phase of the Global Navigation Satellite Systems (GNSS) signals. Ionospheric scintillation severely degrades the performance of the GNSS receiver in the signal acquisition, tracking, and positioning. By utilizing the GNSS signals, detecting and monitoring the scintillation effects to decrease the effect of the disturbing signals have gained importance, and machine learning-based algorithms have been started to be applied for the detection. In this paper, the performance of Support Vector Machines (SVM) for scintillation detection is discussed. The effect of the different kernel functions, namely, linear, Gaussian, and polynomial, on the performance of the SVM algorithm is analyzed. Performance is statistically assessed in terms of probabilities of detection and false alarm of the scintillation event. Real GNSS signals that are affected by significant phase and amplitude scintillation effect, collected at the South African Antarctic research base SANAE IV and Hanoi, Vietnam have been used in this study. This paper questions how to select a suitable kernel function by analyzing the data preparation, cross-validation, and experimental test stages of the SVM-based process for scintillation detection. It has been observed that the overall accuracy of fine Gaussian SVM outperforms the linear, which has the lowest complexity and running time. Moreover, the third-order polynomial kernel provides improved performance compared to linear, coarse, and medium Gaussian kernel SVMs, but it comes with a cost of increased complexity and running time

Context-aware Peer-to-Peer and Cooperative Positioning

Peer-to-peer and cooperative positioning represent one of the major evolutions for mass-market positioning, bringing together capabilities of Satellite Navigation and Communication Systems. It is well known that smartphones already provide user position leveraging both GNSS and information collected through the communication network (e.g., Assisted-GNSS). However, exploiting the exchange of information among close users can attain further benefits. In this paper, we deal with such an approach and show that sharing information on the environmental conditions that characterize the reception of satellite signals can be effectively exploited to improve the accuracy and availability of user positioning. This approach extends the positioning service to indoor environments and, in general, to any scenario where full visibility of the satellite constellation cannot be grante

Use of the Wavelet Transform for Interference Detection and Mitigation in Global Navigation Satellite Systems

Radio frequency interference detection and mitigation are becoming of paramount importance due to the increasing number of services and applications based on the position obtained by means of Global Navigation Satellite Systems. A way to cope with such threats is the implementation in the receiver of advanced signal processing algorithm able to raise proper warning or improve the receiver performance. In this paper, we propose a method based on the Wavelet Transform able to split the useful signal from the interfering component in a transformed domain. The wavelet packet decomposition and proper statistical thresholds allow the algorithm to show very good performance in case of multiple pulse interference as well as in the case of narrowband interference, two scenarios in which traditional countermeasures might not be effective

Towards Analyzing the Effect of Interference Monitoring in GNSS Scintillation

Ionospheric Scintillation Monitoring Receivers (ISMR) are specialized GNSS receivers able to track and monitor scintillations in order to collect data that can be used to model the phenomenon, study its affects at receiver level and possibly predict its occurrence in the future. Such receivers are able to measure the amount of scintillation affecting a satellite signal in both amplitude and phase by making use of correlation data from the tracking processing blocks. This is normally done by computing two indices: the S4 for amplitude scintillation and the phase deviation due to scintillations [3]. However, as more telecommunication systems are likely to work in frequency bands close to GNSS signals in the next years, monitoring of scintillation activity might be threatened by the presence of Radio Frequency Interference (RFI) in the operation area. It is of interest to study the effects these systems may have on the estimation of scintillation indices due to unintentional leakages of power out of their allocated bandwidth [4]. Robust tracking of GNSS signals under such conditions must be guaranteed and it must also be ensured as best as possible that the typical scintillation indices are not affected by the additional error sourc

Use of the Karhunen-Loève Transform for interference detection and mitigation in GNSS

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

A Deep Neural Network Approach for Classification of GNSS Interference and Jammer

Global Navigation Satellite Systems (GNSS) are one of the most important infrastructures in the modern world for positioning and timing, also enabling many critical applications that require the reliability of the received signals. However, it is well known that the power of the GNSS signals at the receiver's antenna is extremely weak, and radio-frequency interference affecting the GNSS bandwidths might lead to reduced positioning and timing accuracy or even a complete lack of the navigation solution. Therefore, in order to mitigate interference in the GNSS receivers and guarantee reliable solutions, interference classification becomes of paramount importance. This paper proposes an approach for the automatic and accurate classification of the most common interference and jammers based on the use of Convolutional Neural Networks (CNN). The input for the network is the time-frequency representation of the received signal, together with features in the time and frequency domains. The time-frequency representation is obtained using both the Wigner-Ville and the short-time Fourier transforms. Moreover, the performance of the proposed method is compared using two different CNN architectures, AlexNet and ResNet. The effectiveness of the method is shown in two case studies: Monitoring and classification by a terrestrial station and from a Low Earth Orbit (LEO) satellite. The results reveal that the proposed method achieves a high accuracy of 99.69% in classifying interference, even with low interference power, and can be implemented as a real-time tool for monitoring jammers

Detection and Classification of {GNSS} Jammers Using Convolutional Neural Networks

Global Navigation Satellite Systems (GNSSs) have been established as one of the most significant infrastructures in today's world and play an important role in many critical applications. It is known that the power of the GNSS signals at the receivers' antenna is extremely weak and the transmitted signals are vulnerable to interference, which can cause degraded positioning and timing accuracy or even a complete lack of position availability. Thus, it is essential for GNSS applications to detect interference and further recognize the types of it for the mitigation in GNSS receivers to guarantee reliable solutions. In this paper, the focus is on the automatic detection and classi-fication of chirp signals, known as one of the most common and disruptive interfering signals. The classifier is a Convolutional Neural Networks (CNN) based on multi-layer neural networks that operate on the representation of the signals in transformed domains, Wigner- Ville and Short Time Fourier transforms. The representation of signals is fed to a CNN algorithm to classify the different shapes of chirp signals. The proposed method is performed in two case-study scenarios: the monitoring and classification by a terrestrial interference monitor and from a Low-Earth-Orbit (LEO) satellite. The experimental results demonstrate that the CNN model has a classification accuracy of 93 % and can be a suitable approach to classify different shapes of chirp signals

On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection

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