## Statistical Signal Processing for Data Fusion (2012)

Ultra Wideband Communications: from Analog to Digital

The aim of this thesis is to investigate key issues encountered in the design of transmission schemes and receiving techniques for Ultra Wideband (UWB) communication systems. Based on different data rate applications, this work is divided into two parts, where energy efficient and robust physical layer solutions are proposed, respectively. Due to a huge bandwidth of UWB signals, a considerable amount of multipath arrivals with various path gains is resolvable at the receiver. For low data rate impulse radio UWB systems, suboptimal non-coherent detection is a simple way to effectively capture the multipath energy. Feasible techniques that increase the power efficiency and the interference robustness of non-coherent detection need to be investigated. For high data rate direct sequence UWB systems, a large number of multipath arrivals results in severe inter-/intra-symbol interference. Additionally, the system performance may also be deteriorated by ...

Song, Nuan — Ilmenau University of Technology

Bayesian Fusion of Multi-band Images: A Powerful Tool for Super-resolution

Hyperspectral (HS) imaging, which consists of acquiring a same scene in several hundreds of contiguous spectral bands (a three dimensional data cube), has opened a new range of relevant applications, such as target detection [MS02], classification [C.-03] and spectral unmixing [BDPD+12]. However, while HS sensors provide abundant spectral information, their spatial resolution is generally more limited. Thus, fusing the HS image with other highly resolved images of the same scene, such as multispectral (MS) or panchromatic (PAN) images is an interesting problem. The problem of fusing a high spectral and low spatial resolution image with an auxiliary image of higher spatial but lower spectral resolution, also known as multi-resolution image fusion, has been explored for many years [AMV+11]. From an application point of view, this problem is also important as motivated by recent national programs, e.g., the Japanese next-generation space-borne ...

Wei, Qi — University of Toulouse

Bayesian data fusion for distributed learning

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 ...

Peng Wu — Northeastern University

Massive MIMO: Fundamentals and System Designs

The last ten years have seen a massive growth in the number of connected wireless devices. Billions of devices are connected and managed by wireless networks. At the same time, each device needs a high throughput to support applications such as voice, real-time video, movies, and games. Demands for wireless throughput and the number of wireless devices will always increase. In addition, there is a growing concern about energy consumption of wireless communication systems. Thus, future wireless systems have to satisfy three main requirements: i) having a high throughput; ii) simultaneously serving many users; and iii) having less energy consumption. Massive multiple-input-multiple-output (MIMO) technology, where a base station (BS) equipped with very large number of antennas (collocated or distributed) serves many users in the same time-frequency resource, can meet the above requirements, and hence, it is a promising candidate technology ...

Ngo, Quoc Hien — Linköping University

The thesis addresses the problem of space-time codebook design for communication in multiple-input multiple-output (MIMO) wireless systems. The realistic and challenging non-coherent setup (channel state information is absent at the receiver) is considered. A generalized likelihood ratio test (GLRT)-like detector is assumed at the receiver and contrary to most existing approaches, an arbitrary correlation structure is allowed for the additive Gaussian observation noise. A theoretical analysis of the probability of error is derived, for both the high and low signal-to-noise ratio (SNR) regimes. This leads to a codebook design criterion which shows that optimal codebooks correspond to optimal packings in a Cartesian product of projective spaces. The actual construction of the codebooks involves solving a high-dimensional, nonlinear, nonsmooth optimization problem which is tackled here in two phases: a convex semi-definite programming (SDP) relaxation furnishes an initial point which is then ...

Beko, Marko — IST, Lisbon

Parametric and non-parametric approaches for multisensor data fusion

Multisensor data fusion technology combines data and information from multiple sensors to achieve improved accuracies and better inference about the environment than could be achieved by the use of a single sensor alone. In this dissertation, we propose parametric and nonparametric multisensor data fusion algorithms with a broad range of applications. Image registration is a vital first step in fusing sensor data. Among the wide range of registration techniques that have been developed for various applications, mutual information based registration algorithms have been accepted as one of the most accurate and robust methods. Inspired by the mutual information based approaches, we propose to use the joint RÂ´enyi entropy as the dissimilarity metric between images. Since the RÂ´enyi entropy of an image can be estimated with the length of the minimum spanning tree over the corresponding graph, the proposed information-theoretic registration ...

Ma, Bing — University of Michigan

Advances in Detection and Classification for Through-the-Wall Radar Imaging

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 ...

Debes, Christian — Technical University of Darmstad

MIMO Radars with Sparse Sensing

Multi-input and multi-output (MIMO) radars achieve high resolution of arrival direction by transmitting orthogonal waveforms, performing matched filtering at the receiver end and then jointly processing the measurements of all receive antennas. This dissertation studies the use of compressive sensing (CS) and matrix completion (MC) techniques as means of reducing the amount of data that need to be collected by a MIMO radar system, without sacrificing the system’s good resolution properties. MIMO radars with sparse sensing are useful in networked radar scenarios, in which the joint processing of the measurements is done at a fusion center, which might be connected to the receive antennas via a wireless link. In such scenarios, reduced amount of data translates into bandwidth and power saving in the receiver-fusion center link. First, we consider previously defined CS-based MIMO radar schemes, and propose optimal transmit antenna ...

Sun, Shunqiao — Rutgers, The State University of New Jersey

Phase Noise and Wideband Transmission in Massive MIMO

In the last decades the world has experienced a massive growth in the demand for wireless services. The recent popularity of hand-held devices with data exchange capabilities over wireless networks, such as smartphones and tablets, increased the wireless data traffic even further. This trend is not expected to cease in the foreseeable future. In fact, it is expected to accelerate as everyday apparatus unrelated with data communications, such as vehicles or household devices, are foreseen to be equipped with wireless communication capabilities. Further, the next generation wireless networks should be designed such that they have increased spectral and energy efficiency, provide uniformly good service to all of the accommodated users and handle many more devices simultaneously. Massive multiple-input multiple-output (Massive MIMO) systems, also termed as large-scale MIMO, very large MIMO or full-dimension MIMO, have recently been proposed as a candidate ...

Pitarokoilis, Antonios — Linköping University

Distributed Detection and Localization

This thesis delves into the detection and localization aspects of distributed Wireless Sensor Networks (WSNs). Specifically, the research concentrates on WSNs in which sensors autonomously carry out detection tasks and transmit their decisions to a fusion center (FC). The FC’s role is to make a comprehensive decision about the presence of a specific event of interest and estimate its potential location. Given its broad significance, the thesis specializes in applying WSNs for industrial monitoring, particularly in the process and energy industry. Three distinct approaches are explored in this thesis: (i) per-sample/batch detection, (ii) quickest detection, and (iii) sequential detection. Each framework proposes a set of detection and associated localization rules. A primary objective of this work is to develop detection and localization strategies that leverage existing information about the monitored environment, bridging the gap between monitoring systems and the knowledge ...

Gianluca Tabella — Norwegian University of Science and Technology

Generalized Noncoherent Ultra-Wideband Receivers

This thesis investigates noncoherent multi-channel ultra-wideband receivers. Noncoherent ultra-wideband receivers promise low power consumption and low processing complexity as they, in contrast to coherent receiver architectures, relinquish the need of complex carrier frequency and phase recovering. Unfortunately, their peak data rate is limited by the delay spread of the multipath radio channel. Noncoherent multi-channel receivers can break this rate limit due to their capability to demodulate multi-carrier signals. Such receivers use an analog front-end to separate the received signals into their sub-channels. In this work, the modeling and optimization of realistic front-end components is addressed and their impact on the system performance of noncoherent multi-channel ultra-wideband receivers is analyzed. With a proposed generalized mathematical framework, it is shown that there exists a variety of noncoherent multi-channel receiver types with similar system performance which differ only in their front-end filters. It ...

Pedroß-Engel, Andreas — Graz University of Technology

Resistivity distribution estimation, widely known as Electrical Impedance Tomography (EIT), is a non linear ill-posed inverse problem. However, the partial derivative equation ruling this experiment yields no analytical solution for arbitrary conductivity distribution. Thus, solving the forward problem requires an approximation. The Finite Element Method (FEM) provides us with a computationally cheap forward model which preserves the non linear image-data relation and also reveals sufficiently accurate for the inversion. Within the Bayesian approach, Markovian priors on the log-conductivity distribution are introduced for regularization. The neighborhood system is directly derived from the FEM triangular mesh structure. We first propose a maximum a posteriori (MAP) estimation with a Huber-Markov prior which favours smooth distributions while preserving locally discontinuous features. The resulting criterion is minimized with the pseudo-conjugate gradient method. Simulation results reveal significant improvements in terms of robustness to noise, computation rapidity ...

Martin, Thierry — Laboratoire des signaux et systèmes

In a communication system it results undoubtedly of great interest to compress the information generated by the data sources to its most elementary representation, so that the amount of power necessary for reliable communications can be reduced. It is often the case that the redundancy shown by a wide variety of information sources can be modelled by taking into account the probabilistic dependance among consecutive source symbols rather than the probabilistic distribution of a single symbol. These sources are commonly referred to as single or multiterminal sources "with memory" being the memory, in this latter case, the existing temporal correlation among the consecutive symbol vectors generated by the multiterminal source. It is well known that, when the source has memory, the average amount of information per source symbol is given by the entropy rate, which is lower than its entropy ...

Del Ser, Javier — University of Navarra (TECNUN)

Large Multiuser MIMO Detection: Algorithms and Architectures

After decades of research on multiple-input multiple-output (MIMO) technology, including paradigm shifts from point-to-point to multiuser MIMO (MU-MIMO), an ample literature exists on techniques to exploit the spatial dimension to increase link throughput and network capacity of wireless communication systems. Massive MIMO, which supports hundreds of antennas at the base station (BS), is celebrated as the key enabling technology of the upcoming fifth generation (5G) wireless communication standard. However, the use of large MIMO systems in the future is also indispensable, especially for high-speed wireless backhaul connectivity. Large MIMO systems use tens of antennas in communication terminals, and can afford a large number of antennas on both the transmitter and the receiver sides. While favorable propagation in massive MIMO ensures that reliable performance can be achieved by simple linear processing, the inherent symmetry in large MIMO renders the computational complexity ...

Sarieddeen, Hadi — American University of Beirut (AUB)

Detection and Resource Allocation Algorithms for Cooperative MIMO Relay Systems

Cooperative communications and multiple-input multiple-output (MIMO) communication systems are important topics in current research that will play key roles in the future of wireless networks and standards. These techniques can provide gains in data throughput, network capacity, coverage, outage, reduced error rates and power consumption, but can have an increased cost in computational complexity and present new problems in many areas. In this thesis, the various challenges in accurately detecting and estimating data signals and allocating resources in the cooperative systems are investigated. Firstly, we propose a cross-layer design strategy that consists of a cooperative maximum likelihood (ML) detector operating in conjunction with link selection for a cooperative MIMO network. The cooperative ML detector is derived, with considerations and approximations made for the knowledge of the system information that is available to the detector. Link selection in the cooperative network ...

Hesketh, Thomas John — University of York

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