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, ...

The first part of this dissertation considers distributed learning problems over networked agents. The general objective of distributed adaptation and learning is the solution of global, stochastic optimization problems through localized interactions and without information about the statistical properties of the data. Regularization is a useful technique to encourage or enforce structural properties on the resulting solution, such as sparsity or constraints. A substantial number of regularizers are inherently non-smooth, while many cost functions are differentiable. We propose distributed and adaptive strategies that are able to minimize aggregate sums of objectives. In doing so, we exploit the structure of the individual objectives as sums of differentiable costs and non-differentiable regularizers. The resulting algorithms are adaptive in nature and able to continuously track drifts in the problem; their recursions, however, are subject to persistent perturbations arising from the stochastic nature of ...

In Wireless Sensor Networks (WSN), the ability of sensor nodes to know its position is an enabler for a wide variety of applications for monitoring, control, and automation. Often, sensor data is meaningful only if its position can be determined. Many WSN are deployed indoors or in areas where Global Navigation Satellite System (GNSS) signal coverage is not available, and thus GNSS positioning cannot be guaranteed. In these scenarios, WSN may be relied upon to achieve a satisfactory degree of positioning accuracy. Typically, batteries power sensor nodes in WSN. These batteries are costly to replace. Therefore, power consumption is an important aspect, being performance and lifetime ofWSN strongly relying on the ability to reduce it. It is crucial to design effective strategies to maximize battery lifetime. Optimization of power consumption can be made at different layers. For example, at the ...

The level of automation in our society is ever increasing. Technologies like self-driving cars, virtual reality, and fully autonomous robots, which all were unimaginable a few decades ago, are realizable today, and will become standard consumer products in the future. These technologies depend upon autonomous localization and situation awareness where careful processing of sensory data is required. To increase efficiency, robustness and reliability, appropriate models for these data are needed. In this thesis, such models are analyzed within three different application areas, namely (1) magnetic localization, (2) extended target tracking, and (3) autonomous learning from raw pixel information. Magnetic localization is based on one or more magnetometers measuring the induced magnetic field from magnetic objects. In this thesis we present a model for determining the position and the orientation of small magnets with an accuracy of a few millimeters. This ...

Global Navigation Satellite Systems (GNSS)-based localization and attitude determination are essential for many navigation and control systems widely used in aircrafts, spacecrafts, vessels, automobiles, and other dynamic platforms. A GNSS receiver can generate pseudo-range and carrier-phase observations based on the signals transmitted from the navigation satellites. Since the accuracy of the carrier phase is two orders of magnitude higher than that of the pseudo-range, it is crucial to employ the precise GNSS data, the carrier phase, to perform a high-accuracy position or/and attitude estimate. The main challenge to fully utilizing carrier-phase observations is to successfully resolve the unknown integer parts (number of whole cycles), a process usually referred to as integer ambiguity resolution. Many methods have been developed to resolve integer ambiguities with different performance offerings. Under challenging environments with insufficient tracked satellites, significant multipath interference, and severe atmospheric effects, ...

Indoor location has experienced a major boost in recent years. location based services (LBS), which until recently were restricted to outdoor scenarios and the use of GPS, have also been extended into buildings. From large public structures such as airports or hospitals to a multitude of industrial scenarios, LBS has become increasingly present in indoor scenarios. Of the various technologies that can be used to achieve this indoor location, the ones based on ultra- wideband (UWB) signals have become ones of the most demanded due primarily to their accuracy in position estimation. Additionally, the appearance in the market of more and more manufacturers and products has lowered the prices of these devices to levels that allow to think about their use for large deployments with a contained budget. By their nature, UWB signals are very resistant to the multi-path phenomenon, ...

Distributed signal or information processing using Internet of Things (IoT), facilitates real-time monitoring of signals, for example, environmental pollutants, health indicators, and electric energy consumption in a smart city. Despite the promising capabilities of IoTs, these distributed deployments often face the challenge of data privacy and communication rate constraints. In traditional machine learning, training data is moved to a data center, which requires massive data movement from distributed IoT devices to a third-party location, thus raising concerns over privacy and inefficient use of communication resources. Moreover, the growing network size, model size, and data volume combined lead to unusual complexity in the design of optimization algorithms beyond the compute capability of a single device. This necessitates novel system architectures to ensure stable and secure operations of such networks. Federated learning (FL) architecture, a novel distributed learning paradigm introduced by McMahan ...

The unprecedented processing demand, posed by the explosion of big data, challenges researchers to design efficient and adaptive machine learning algorithms that do not require persistent retraining and avoid learning redundant information. Inspired from learning techniques of intelligent biological agents, identifying transferable knowledge across learning problems has been a significant research focus to improve machine learning algorithms. In this thesis, we address the challenges of knowledge transfer through embedding spaces that capture and store hierarchical knowledge. In the first part of the thesis, we focus on the problem of cross-domain knowledge transfer. We first address zero-shot image classification, where the goal is to identify images from unseen classes using semantic descriptions of these classes. We train two coupled dictionaries which align visual and semantic domains via an intermediate embedding space. We then extend this idea by training deep networks that ...

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 ...

In this dissertation we focus on statistical signal processing for Data Fusion, with a particular focus on wireless sensor networks. Six topics are studied: (i) Data Fusion for classification under model uncertainty; (ii) Decision Fusion over coherent MIMO channels; (iii) Performance analysis of Maximum Ratio Combining in MIMO decision fusion; (iv) Decision Fusion over non-coherent MIMO channels; (v) Decision Fusion for distributed classification of multiple targets; (vi) Data Fusion for inverse localization problems, with application to wideband passive sonar platform estimation. The first topic of this thesis addresses the problem of lack of knowledge of the prior distribution in classification problems that operate on small data sets that may make the application of Bayes' rule questionable. Uniform or arbitrary priors may provide classification answers that, even in simple examples, may end up contradicting our common sense about the problem. Entropic ...

Automatic modulation classification detects the modulation type of received communication signals. It has important applications in military scenarios to facilitate jamming, intelligence, surveillance, and threat analysis. The renewed interest from civilian scenes has been fueled by the development of intelligent communications systems such as cognitive radio and software defined radio. More specifically, it is complementary to adaptive modulation and coding where a modulation can be deployed from a set of candidates according to the channel condition and system specification for improved spectrum efficiency and link reliability. In this research, we started by improving some existing methods for higher classification accuracy but lower complexity. Machine learning techniques such as k-nearest neighbour and support vector machine have been adopted for simplified decision making using known features. Logistic regression, genetic algorithm and genetic programming have been incorporated for improved classification performance through feature ...

This dissertation explores advancements in future navigation satellite systems, proposing and analyzing solutions at system, signals, and user level. The objective of this work has been to seek for performance improvements, acting at various levels of the Global Navigation Satellite System (GNSS) value chain, yet fulfilling possible upcoming needs and constraints. In this context, this work focuses on improving the use of resources, both upstream, to enhance signals and services, and downstream, by leveraging such signals for a better user performance. Specific research questions were addressed for this purpose: how can inter-satellite links (ISLs) be assigned while optimizing data and navigation performance? Can multiple signals transmission be more efficient? How can we leverage signal multiplicity and receiver technologies to improve accuracy and robustness of the final position, velocity, and time (PVT) estimation? The first part focuses on space segment evolution, ...

In this PhD thesis the problem of detection and classification of stationary targets in Through-the-Wall Radar Imaging is considered. A multiple-view framework is used in which a 3D scene of interest is imaged from a set of vantage points. By doing so, clutter and noise is strongly suppressed and target detectability increased. In target detection, centralized as well as decentralized frameworks for simultaneous image fusion and detection are examined. The practical case when no prior knowledge on image statistics is available and all inference must be drawn from the data at hand is specifically considered. An adaptive detection scheme is proposed which iteratively adapts in a non-stationary environment. Optimal configurations for this scheme are derived based on morphological operations which allow for automatic and reliable target detection. In a decentralized framework, local decisions are transmitted to a fusion center to ...

In the modern world, we are increasingly surrounded by computation devices with communication links and one or more microphones. Such devices are, for example, smartphones, tablets, laptops or hearing aids. These devices can work together as nodes in an acoustic sensor network (ASN). Such networks are a growing platform that opens the possibility for many practical applications. ASN based speech enhancement, source localization, and event detection can be applied for teleconferencing, camera control, automation, or assisted living. For this kind of applications, the awareness of auditory objects and their spatial positioning are key properties. In order to provide these two kinds of information, novel methods have been developed in this thesis. Information on the type of auditory objects is provided by a novel real-time sound classification method. Information on the position of human speakers is provided by a novel localization ...

During the last decades, information is being gathered and processed at an explosive rate. This fact gives rise to a very important issue, that is, how to effectively and precisely describe the information content of a given source signal or an ensemble of source signals, such that it can be stored, processed or transmitted by taking into consideration the limitations and capabilities of the several digital devices. One of the fundamental principles of signal processing for decades is the Nyquist-Shannon sampling theorem, which states that the minimum number of samples needed to reconstruct a signal without error is dictated by its bandwidth. However, there are many cases in our everyday life in which sampling at the Nyquist rate results in too many data and thus, demanding an increased processing power, as well as storage requirements. A mathematical theory that emerged ...