Intentional interference in satellite navigation is becoming an increasing threat for modern systems relying on Global Navigation Satellite Systems (GNSS). In particular, critical applications such as aviation can be severely affected by undetected and un-mitigated interference and therefore interference management solutions are crucial to be employed. Methods to cope with such intentional interference enclose interference detection, interference mitigation, interference classification, and interference localization. This paper offers a comprehensive survey of interference management methods developed in the last four decades by the research community. After reviewing the main concepts of GNSS-based navigation, the interference and interference management solutions are classified, with a particular focus on the two major threats in GNSS navigation, namely jamming and spoofing. Mathematical models, comparative tables for various interference management solutions, such as detection, localization, mitigation, and classification, as well as comparative numerical results based on several selected algorithms are also presented. We especially focus on algorithms relying on omnidirectional antennas, which do not require additional specific antennas to be installed on the aircraft and thus reduce the costs of retrofit and installation.

More and more satellites are populating the sky nowadays in the Low Earth orbits (LEO). Most of the targeted applications are related to broadband and narrowband communications, Earth observation, synthetic aperture radar, and internet-of-Things (IoT) connectivity. In addition to these targeted applications, there is yet-to-be-harnessed potential for LEO and positioning, navigation, and timing (PNT) systems, or what is nowadays referred to as LEO-PNT. No commercial LEO-PNT solutions currently exist and there is no unified research on LEO-PNT concepts. Our survey aims to fill the gaps in knowledge regarding what a LEO-PNT system entails, its technical design steps and challenges, what physical layer parameters are viable solutions, what tools can be used for a LEO-PNT design (e.g., optimisation steps, hardware and software simulators, etc.), the existing models of wireless channels for satellite-toground and ground-to-satellite propagation, and the commercial prospects of a future LEO-PNT system. A comprehensive and multidisciplinary survey is provided by a team of authors with complementary expertise in wireless communications, signal processing, navigation and tracking, physics, machine learning, Earth observation, remote sensing, digital economy, and business models.

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 Dopplerbased 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.

This paper proposes to treat the jammer classification problem in the Global Navigation Satellite System bands as a black-and-white image classification problem, based on a time-frequency analysis and image mapping of a jammed signal. The paper also proposes to apply machine learning approaches in order to sort the received signal into six classes, namely five classes when the jammer is present with different jammer types and one class where the jammer is absent. The algorithms based on support vector machines show up to 94.90% accuracy in classification, and the algorithms based on convolutional neural networks show up to 91.36% accuracy in classification. The training and test databases generated for these tests are also provided in open access.

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.