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

Comparison of MEO, LEO, and Terrestrial IoT Configurations in Terms of GDOP and Achievable Positioning Accuracies

Complementary solutions to the Medium Earth Orbit (MEO) Global Navigation Satellite Systems (GNSS) are more and more in demand to be able to achieve seamless positioning worldwide, in outdoor as well as in indoor scenarios, and to cope with increased interference threats in GNSS bands. Two of such complementary systems can rely on the emerging Low Earth Orbit (LEO) constellations and on the terrestrial long-range Internet of Things (IoT) systems, both under rapid developments nowadays. Standalone positioning solutions based on such systems complementary to GNSS can be beneficial in situations where GNSS signal is highly affected by interferences, such as jammers and spoofers, while hybrid GNSS and nonGNSS solutions making use of LEO and terrestrial IoT signals as signals of opportunity can improve the achievable positioning accuracy in a wide variety of scenarios. Comparative research of performance bounds achievable through MEO, LEO, and terrestrial IoT signals are still hard to find in the current literature. It is the goal of this paper to introduce a unified framework to compare these three system types, based on geometry matrices and error modeling, and to present a performance analysis in terms of Geometric Dilution of Precision (GDOP) and positioning accuracy bounds.Peer reviewe

Performance analysis for wireless G (IEEE 802.11G) and wireless N (IEEE 802.11N) in outdoor environment

This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. The comparison consider on coverage area (mobility), throughput and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g

Performance Analysis For Wireless G (IEEE 802.11 G) And Wireless N (IEEE 802.11 N) In Outdoor Environment

This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. the comparison consider on coverage area (mobility), through put and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g