The provision of accurate positioning is becoming essential to our modern society. One of the main reasons is the great success and ease of use of Global Navigation Satellite Systems (GNSSs), which has led to an unprecedented amount of GNSS-based applications. In particular, the current trend shows that a new era of GNSS-based applications and services is emerging. These applications are the so-called critical applications, in which the physical safety of users may be in danger due to a miss-performance of the positioning system. These applications have very stringent requirements in terms of integrity. Integrity is a measure of reliability and trust that can be placed on the information provided by the system. Integrity algorithms were originally designed for civil aviation in the 1980s. Unfortunately, GNSS-based critical applications are often associated with terrestrial environments and original integrity algorithms usually fail. ...

This dissertation investigates robust signal processing and machine learning techniques, with the objective of improving the robustness of two applications against various threats, namely Global Navigation Satellite System (GNSS) based positioning and satellite imaging. GNSS technology is widely used in different fields, such as autonomous navigation, asset tracking, or smartphone positioning, while the satellite imaging plays a central role in monitoring, detecting and estimating the intensity of key natural phenomena, such as flooding prediction and earthquake detection. Considering the use of both GNSS positioning and satellite imaging in critical and safety-of-life applications, it is necessary to protect those two technologies from either intentional or unintentional threats. In the real world, the common threats to GNSS technology include multipath propagation and intentional/unintentional interferences. This thesis investigates methods to mitigate the influence of such sources of error, with the final objective of ...

Global Navigation Satellite Systems (GNSS) is the de facto technology for Position, Navigation, and Timing (PNT) applications when it is available. GNSS relies on one or more satellite constellations that transmit ranging signals, which a receiver can use to self-localize. Signal acquisition is a crucial step in GNSS receivers, which is typically solved by maximizing the so-called Cross Ambiguity Function (CAF) resulting from a hypothesis testing problem. The CAF is a two-dimensional function that is related to the correlation between the received signal and a local code replica for every possible delay/Doppler pair, which is then maximized for signal detection and coarse synchronization. The outcome of this statistical process decides whether the signal from a particular satellite is present or absent in the received signal, as well as provides a rough estimate of its associated code delay and Doppler frequency, ...

This thesis deals with estimation of states and parameters in nonlinear and non-Gaussian dynamic systems. Sequential Monte Carlo methods are mainly used to this end. These methods rely on models of the underlying system, motivating some developments of the model concept. One of the main reasons for the interest in nonlinear estimation is that problems of this kind arise naturally in many important applications. Several applications of nonlinear estimation are studied. The models most commonly used for estimation are based on stochastic difference equations, referred to as state-space models. This thesis is mainly concerned with models of this kind. However, there will be a brief digression from this, in the treatment of the mathematically more intricate differential-algebraic equations. Here, the purpose is to write these equations in a form suitable for statistical signal processing. The nonlinear state estimation problem is ...

Snapshot positioning is the technique to determine the position of a Global Navigation Satellite System (GNSS) receiver using only a very brief interval of the received satellite signal. In recent years, this technique has received a great amount of attention thanks to its unique advantages in power efficiency, Time To First Fix (TTFF) and economic costs for deployment. However, the state of the art algorithms regarding snapshot positioning were based on code measurements only, which unavoidably limited the positioning accuracy to meter level. The present PhD research aims at achieving high-accuracy (centimetre level) snapshot positioning by properly utilizing carrier phase measurements. Two technical challenges should be tackled before such level of accuracy can be achieved, namely, satellite transmission time inaccuracy and the so-called Data Bit Ambiguity (DBA) issue. The first challenge is essentially originated from the lack of absolute timing ...

In the last decades, air traffic spread more and more in the world, connecting more and more places. At the same time, the need to manage all the flights correctly and securely increased. Air traffic authorities imposed and updated several standards for the air traffic management (ATM) system, keeping in pace with the growing traffic flow. To achieve this, special voice communication systems (VCS) were developed. They ensure the communication between the pilots and the operators from the ground control centers. When a communication is initiated between the aircraft’s pilot and the ground air traffic control operator, various systems are used. The pilot speaks through the aircraft’s radio station and the signal is received by several ground radio stations. Then, the signal from each ground radio station arrives on different paths to the control center. Here one of the received ...

Multi-user multi-antenna wireless communication systems have become essential due to the widespread of smart applications and the use of the Internet. Ultra-dense deployment of small cell networks has been recognized as an effective way to meet the exponentially growing mobile data traffic and to accommodate increasingly diversified mobile applications for beyond 5G and future wireless networks. Small cells using low power nodes are meant to be deployed in hot spots, where the number of users varies strongly with time and between adjacent cells. As a result, small cells are expected to have burst-like traffic, which makes the static time division duplex (TDD) frame configuration strategy, where a common TDD pattern is selected for the whole network, not able to meet the users' requirements and the traffic fluctuations. Dynamic TDD (DTDD) technology which allows the cells to independently adapt their TDD ...

The thesis focuses on statistical approaches to model, mitigate, and prevent distributed network attacks. When dealing with distributed network attacks (and, more in general, with cyber-security problems), three fundamental phases/issues emerge distinctly. The first issue concerns the threat propagation across the network, which entails an "avalanche" effect, with the number of infected nodes increasing exponentially as time elapses. The second issue regards the design of proper mitigation strategies (e.g., threat detection, attacker's identification) aimed at containing the propagation phenomenon. Finally (and this is the third issue), it is also desirable to act on the system infrastructure to grant a conservative design by adding some controlled degree of redundancy, in order to face those cases where the attacker has not been yet defeated. The contributions of the present thesis address the aforementioned relevant issues, namely, propagation, mitigation and prevention of distributed ...

Due to some tragic events such as crime, bank robberies and terrorist attacks, an unparalleled surge in video surveillance cameras has occurred in recent years. In consequence, our daily life is overseen everywhere (e.g. on the street, in stations, in shops and in the workplace). For example, on average, people living in London can be caught on cameras more than 300 times a day. At the same time, automatic processing technology and quality of sensors have advanced significantly, which has even enabled automatic detection, tracking and identification of individuals. With the proliferation of video surveillance systems and the progress in automatic recognition, privacy protection is now becoming a significant concern. Video surveillance is intrusive because it allows the observation of certain information that is considered as private (i.e., identity or some characteristics such as age, race, gender). Nowadays, some processing ...

The space environment around planet Earth comprises a variety of nonhomogeneous and nonstationary fluxes of natural and man-made junk. Such debris may collide at hypervelocity with strategic orbital infrastructure, thus jeopardizing the space economy. For this reason, the European Space Agency (ESA) sustains a strategy to acquire a “...capability to watch for objects and natural phenomena that could harm satellites in orbit.” Accordingly, large ground-based radars and optical telescopes allow monitoring debris populations with an average size larger than, say 10 cm, up to Low Earth Orbit (LEO) and Geostationary Orbit (GEO), respectively. In fact, these assets form fence coverage areas along with a grueling data fusion for orbit estimation while coping with limits related to temporal and spatial observation constraints, atmospheric hindrances, and detection performance (especially with respect to small-size targets). Interestingly, an active space-based debris detection and tracking ...

In the last few years Homeland Security (HS) has gained a considerable interest in the research community. From a scientific point of view, it is a difficult task to provide a definition of this research area and to exactly draw up its boundaries. In fact, when we talk about the security and the surveillance, several problems and aspects must be considered. In particular, the following factors play a crucial role and define the complexity level of the considered application field: the number of potential threats can be high and uncertain; the threat detection and identification can be made more complicated by the use of camouflaging techniques; the monitored area is typically wide and it requires a large and heterogeneous sensor network; the surveillance operation is strongly related to the operational scenario, so that it is not possible to define a ...

The number of cameras increases rapidly in squares, shopping centers, railway stations and airport halls. There are hundreds of cameras in the city center of Amsterdam. This is still modest compared to the tens of thousands of cameras in London, where citizens are expected to be filmed by more than three hundred cameras of over thirty separate Closed Circuit Television (CCTV) systems in a single day [84]. These CCTV systems include both publicly owned systems (railway stations, squares, airports) and privately owned systems (shops, banks, hotels). The main purpose of all these cameras is to detect, prevent and monitor crime and anti-social behaviour. Other goals of camera surveillance can be detection of unauthorized access, improvement of service, fire safety, etc. Since the terrorist attack on 9/11, detection and prevention of terrorist activities especially at high profiled locations such as airports, ...

Obstructive sleep apnea (OSA) is among the most prevalent sleep disorders, which is estimated to affect 6 %−19 % of women and 13 %−33 % of men. Besides daytime sleepiness, impaired cognitive functioning and an increased risk for accidents, OSA may lead to obesity, diabetes and cardiovascular diseases (CVD) on the long term. Its prevalence is only expected to rise, as it is linked to aging and excessive body fat. Nevertheless, many patients remain undiagnosed and untreated due to the cumbersome clinical diagnostic procedures. For this, the patient is required to sleep with an extensive set of body attached sensors. In addition, the recordings only provide a single night perspective on the patient in an uncomfortable, and often unknown, environment. Thus, large scale monitoring at home is desired with comfortable sensors, which can stay in place for several nights. To ...

This dissertation explores the intersection of data fusion, federated learning, and Bayesian methods, with a focus on their applications in indoor localization, GNSS, and image processing. Data fusion involves integrating data and knowledge from multiple sources. It becomes essential when data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest. Data fusion typically includes raw data fusion, feature fusion, and decision fusion. In this thesis, we will concentrate on feature fusion. Distributed data fusion involves merging sensor data from different sources to estimate an unknown process. Bayesian framework is often used because it can provide an optimal and explainable feature by preserving the full distribution of the unknown given the data, called posterior, over the estimated process at each agent. This allows for easy and recursive merging of sensor data ...

Device-to-Device (D2D) is one of the important proposed solutions to increase the capacity, offload the traffic, and improve the energy effciency in next generation cellular networks. D2D communication is known as a direct communication between two users without using cellular infrastructure networks. Despite of large expected bene fits in terms of capacity in D2D, the coexistence of D2D and cellular networks in the same spectrum creates new challenges in interference management and network design. To limit the interference power control schemes on cellular networks and D2D networks are typically adopted. Even though power control is introduced to limit the interference level, it does not prevent cellular and D2D users from experiencing coverage limitation when sharing the same radio resources. Therefore, the design of such networks requires the availability of suitable methods able to properly model the eff ect of interference ...