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 ...

In the era of artificial intelligence, there has been a growing consensus that solutions to complex science and engineering problems require novel methodologies that can integrate interpretable physics-based modeling approaches with machine learning techniques, from stochastic optimization to deep neural networks. This thesis aims to develop new methodological and applied frameworks for combining the advantages of physics-based modeling and machine learning, with special attention to two important signal processing tasks: solving inverse problems in hyperspectral imaging and detecting changepoints in time series. The first part of the thesis addresses learning priors in model-based optimization for solving inverse problems in hyperspectral imaging systems. First, we introduce a tuning-free Plug-and-Play algorithm for hyperspectral image deconvolution (HID). Specifically, we decompose the optimization problem into two iterative sub-problems, learn deep priors to solve the blind denoising sub-problem with neural networks, and estimate hyperparameters with ...

Accounting for uncertainty in three-dimensional (3D) shapes is important in a large number of scientific and engineering areas, such as biometrics, biomedical imaging, and data mining. It is well known that 3D polar shaped objects can be represented by Fourier descriptors such as spherical harmonics and double Fourier series. However, the statistics of these spectral shape models have not been widely explored. This thesis studies several areas involved in 3D shape modeling, including random field models for statistical shape modeling, optimal shape filtering, parametric active contours for object segmentation and surface reconstruction. It also investigates multi-modal image registration with respect to tumor activity quantification. Spherical harmonic expansions over the unit sphere not only provide a low dimensional polarimetric parameterization of stochastic shape, but also correspond to the Karhunen-Lo´eve (K-L) expansion of any isotropic random field on the unit sphere. Spherical ...

In this dissertation a notional multi-sensor system acting in a maritime border control scenario for Homeland Security (HS) is analyzed, modelled, and simulated. The functions performed by the system are the detection, tracking, identification and classification of naval targets that enter a sea region, the evaluation of their threat level and the selection of a suitable reaction to them. The emulated system is composed of two platforms carrying multiple sensors: a land based platform, located on the coast, and an air platform, moving on an elliptic trajectory in front of the coast. The land based platform is equipped with a Vessel Traffic Service (VTS) radar, an infrared camera (IR) and a station belonging to an Automatic Identification System (AIS). The air platform carries an Airborne Early Warning Radar (AEWR) that can operate on a spotlight Synthetic Aperture Radar (SAR) mode, ...

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 ...

Super-resolution (SR) is a filtering technique that combines a sequence of under-sampled and degraded low-resolution images to produce an image at a higher resolution. The reconstruction takes advantage of the additional spatio-temporal data available in the sequence of images portraying the same scene. The fundamental problem addressed in super-resolution is a typical example of an inverse problem, wherein multiple low-resolution (LR)images are used to solve for the original high-resolution (HR) image. Super-resolution has already proved useful in many practical cases where multiple frames of the same scene can be obtained, including medical applications, satellite imaging and astronomical observatories. The application of super resolution filtering in consumer cameras and mobile devices shall be possible in the future, especially that the computational and memory resources in these devices are increasing all the time. For that goal, several research problems need to be ...

Multisensor systems are a key enabling technology in, e.g., radar, sonar, medical ultrasound, and wireless communications. Using multiple sensors provides spatial selectivity, improves the signal-to-noise ratio, and enables rejecting unwanted interference. Conventional multisensor systems employ a simple array of uniformly spaced sensors with a linear or rectangular geometry. However, a uniform array spanning a large electrical aperture may become prohibitively expensive, as many sensors and costly RF-IF front ends are needed. In contrast, sparse sensor arrays require drastically fewer resources to achieve comparable performance in terms of spatial resolution and the number of identifiable scatterers or sources. This is facilitated by the co-array: a virtual array structure consisting of the pairwise differences or sums of physical sensor positions. Most recent works on co-array-based sparse array design focus exclusively on passive sensing. Active sensing, where sensors transmit signals and observe their ...

This study compares the performances of various techniques for the differentiation and localization of commonly encountered features in indoor environments, such as planes, corners, edges, and cylinders, possibly with different surface properties, using simple infrared sensors. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting feature in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and differentiation process. The techniques considered include rule-based, template-based, and neural network-based target differentiation, parametric surface differentiation, and statistical pattern recognition techniques such as parametric density estimation, various linear and quadratic classifiers, mixture of normals, kernel estimator, k-nearest neighbor, artificial neural network, and support vector machine classifiers. The geometrical properties of the targets are more distinctive than their surface properties, and surface recognition is the limiting factor ...

The field of Life Science Engineering (LSE) is rapidly expanding and predicted to grow strongly in the next decades. It covers areas of food and medical research, plant and pests’ research, and environmental research. In each research area, engineers try to find equations that model a certain life science problem. Once found, they research different numerical techniques to solve for the unknown variables of these equations. Afterwards, solution improvement is examined by adopting more accurate conventional techniques, or developing novel algorithms. In particular, signal and image processing techniques are widely used to solve those LSE problems require pattern recognition. However, due to the continuous evolution of the life science problems and their natures, these solution techniques can not cover all aspects, and therefore demanding further enhancement and improvement. The thesis presents numerical algorithms of digital signal and image processing to ...

This thesis addresses a target detection problem in radar imaging for which the co- variance matrix of an unknown Gaussian clutter background has block diagonal structure. This block diagonal structure is the consequence of a target lying along a boundary between two statistically independent clutter regions. We consider three di erent assumptions on knowledge of the clutter covariance structure: both clutter types totally unknown, one of the clutter types known except for its variance, and one of the clutter types completely known. Here we design adaptive detection algorithms using both the generalized likelihood ratio (GLR) and the invariance principles. There has been considerable recent interest in applying invariant hypothesis testing as an alternative to the GLR test. This interest has been motivated by several attractive theoretical properties of invariant tests including: exact robustness to variation of nuisance parameters, possible nite-sample ...

Error control coding is an essential part of modern communications systems. LDPC codes have been demonstrated to offer performance near the fundamental limits of channels corrupted by random noise. Optimal maximum likelihood decoding of LDPC codes is too complex to be practically useful even at short block lengths and so a graph-based message passing decoder known as the belief propagation algorithm is used instead. In fact, on graphs without closed paths known as cycles the iterative message passing decoding is known to be optimal and may converge in a single iteration, although identifying the message update schedule which allows single-iteration convergence is not trivial. At finite block lengths graphs without cycles have poor minimum distance properties and perform poorly even under optimal decoding. LDPC codes with large block length have been demonstrated to offer performance close to that predicted for ...

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, ...

Every day we share our personal information through digital systems which are constantly exposed to threats. For this reason, security-oriented disciplines of signal processing have received increasing attention in the last decades: multimedia forensics, digital watermarking, biometrics, network monitoring, steganography and steganalysis are just a few examples. Even though each of these elds has its own peculiarities, they all have to deal with a common problem: the presence of one or more adversaries aiming at making the system fail. Adversarial Signal Processing lays the basis of a general theory that takes into account the impact that the presence of an adversary has on the design of effective signal processing tools. By focusing on the application side of Adversarial Signal Processing, namely adversarial information fusion in distributed sensor networks, and adopting a game-theoretic approach, this thesis contributes to the above mission ...

Direction-of-Arrival (DOA) estimation from data collected at a sensor array in the presence of noise has been a fundamental and long-established research topic of interest in sensor array processing. The application of DOA estimation does not only restrict to radar but also spans multiple additional fields of research, including radio astronomy, biomedical imaging, seismic exploration, wireless communication, among others. Due to the wide applications of DOA estimation, various methods have been developed in the literature to increase the resolution capability, computational efficiency, and robustness of the algorithms. However, a trade-off between the estimation performance and the computational complexity is generally inevitable. This thesis addresses the challenge of developing low-complexity DOA estimators with the ability to resolve closely spaced source signals in the threshold region, i.e., low sample size or low Signal-to-Noise ratio. Motivated by various interpretations of the conventional DOA ...