Compressive Sensing Based Candidate Detector and its Applications to Spectrum Sensing and Through-the-Wall Radar Imaging

Signal acquisition is a main topic in signal processing. The well-known Shannon-Nyquist theorem lies at the heart of any conventional analog to digital converters stating that any signal has to be sampled with a constant frequency which must be at least twice the highest frequency present in the signal in order to perfectly recover the signal. However, the Shannon-Nyquist theorem provides a worst-case rate bound for any bandlimited data. In this context, Compressive Sensing (CS) is a new framework in which data acquisition and data processing are merged. CS allows to compress the data while is sampled by exploiting the sparsity present in many common signals. In so doing, it provides an efficient way to reduce the number of measurements needed for perfect recovery of the signal. CS has exploded in recent years with thousands of technical publications and applications ...

Lagunas, Eva — Universitat Politecnica de Catalunya


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


Advanced GPR data processing algorithms for detection of anti-personnel landmines

Ground Penetrating Radar (GPR) is seen as one of several promising technologies aimed to help mine detection. GPR is sensitive to any inhomogeneity in the ground. Therefore any APM regardless of the metal content can be detected. On the other hand, all the inhomogeneities, which do not represent mines, show up as a clutter in GPR images. Moreover, it is known that reflectivity of APM is often weaker than that of stones, pieces of shrapnel and barbed wire, etc. Altogether these factors cause GPR to produce unacceptably high false alarm rate whilst it reaches the 99.6% detection rate which is prescribed by an UN resolution as a standard for humanitarian demining. The main goal of the work presented in the thesis is reduction of the false alarm rate while keeping the 99.6% detection rate intact. To reach this goal a ...

Kovalenko, Vsevolod — Delft University of Technology


Bayesian Compressed Sensing using Alpha-Stable Distributions

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 ...

Tzagkarakis, George — University of Crete


Direction of Arrival Estimation and Localization Exploiting Sparse and One-Bit Sampling

Data acquisition is a necessary first step in digital signal processing applications such as radar, wireless communications and array processing. Traditionally, this process is performed by uniformly sampling signals at a frequency above the Nyquist rate and converting the resulting samples into digital numeric values through high-resolution amplitude quantization. While the traditional approach to data acquisition is straightforward and extremely well-proven, it may be either impractical or impossible in many modern applications due to the existing fundamental trade-off between sampling rate, amplitude quantization precision, implementation costs, and usage of physical resources, e.g. bandwidth and power consumption. Motivated by this fact, system designers have recently proposed exploiting sparse and few-bit quantized sampling instead of the traditional way of data acquisition in order to reduce implementation costs and usage of physical resources in such applications. However, before transition from the tradition data ...

Saeid Sedighi — University of Luxembourg


Adaptive target detection in radar imaging

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 ...

Kim, Hyung Soo — University of Michigan


Statistical Signal Processing for Data Fusion

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 ...

Ciuonzo, Domenico — Second University of Naples


Cooperative and Cognitive Communication Techniques for Wireless Networks

During the past years wireless communications have been exhibiting an increased growth rendering them the most common way for communication. The continuously increasing demand for wireless services resulted in limited availability of the wireless spectrum. To this end, Cognitive Radio (CR) techniques have been proposed in literature during the past years. The concept of CR approach is to utilize advanced radio and signal-processing technology along with novel spectrum allocation policies to enable new unlicensed wireless users to operate in the existing occupied spectrum areas without degrading the performance of the existing licensed ones. Moreover, the broadcast and fading nature of the wireless channel results in severe degradation on the performance of wireless transmissions. A solution to the problem is the use of multiple-antenna systems so as to achieve spatial diversity. However, in many cases, the communication devices' nature permit the ...

Tsinos, Christos — University of Patras


Signal Design for Active Sensing and Communications

Man-made active sensing systems such as active radar and sonar have been a vital part of our civilization's advancement in navigation, defense, meteorology, and space exploration. Modern active sensing systems rely heavily on the significant progress in the science and technology of communications made within the last century. Not surprising, the fast growing communications technology has changed each and every aspect of our everyday lives. This thesis is concerned with signal design for improving the performance of active sensing and communication systems: The target detection and estimation performance of the active sensing systems can be considerably improved by a judicious design of the probing signals. Similarly, signal design has a crucial role in the implementation and efficiency of communication systems. Signal optimization for active sensing and communications usually deals with various measures of quality. This thesis focuses on several quality ...

Soltanalian, Mojtaba — Uppsala University


Sparse sensor arrays for active sensing - Array configurations and signal processing

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 ...

Robin Rajamäki — Aalto University


Bayesian State-Space Modelling of Spatio-Temporal Non-Gaussian Radar Returns

Radar backscatter from an ocean surface is commonly referred to as sea clutter. Any radar backscatter not due to the scattering from an ocean surface constitutes a potential target. This thesis is concerned with the study of target detection techniques in the presence of high resolution sea clutter. In this dissertation, the high resolution sea clutter is treated as a compound process, where a fast oscillating speckle component is modulated in power by a slowly varying modulating component. While the short term temporal correlations of the clutter are associated with the speckle, the spatial correlations are largely associated with the modulating component. Due to the disparate statistical and correlation properties of the two components, a piecemeal approach is adopted throughout this thesis, whereby the spatial and the temporal correlations of high resolution sea clutter are treated independently. As an extension ...

Noga, Jacek Leszek — University of Cambridge


Study and optimization of multi-antenna systems associated with multicarrier modulations

Since several years, multi-antenna systems are foreseen as a potential solution for increasing the throughput of future wireless communication systems. The aim of this thesis is to study and to improve the transmitter and receiver's techniques of these MIMO (Multiple Input Multiple Output) systems in the context of a multi-carrier transmission. On the one hand, the OFDM (Orthogonal Frequency Division Multiplex) modulation, which transform a frequency selective channel into multiple non frequency selective channels, is particularly well adapted to the conception of MIMO receivers with low complexity. On the other hand, two techniques allowing to improve the exploitation of frequential and/or temporal diversities are associated with OFDM, namely linear precoding (LP-OFDM) and CDMA in a MC-CDMA (Multicarrier Code division Multiplex Access) scheme. We have associated LP-OFDM and MC-CDMA with two MIMO techniques which require no channel state information at the ...

LE NIR, Vincent — INSA de Rennes


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)


Selected Topics In Direct Geolocation Of Radio Transmitters & Passive Targets

This dissertation is dedicated to the exploration of various direct positioning algorithms for radio transmitters and passive target geolocation. Contrary to the traditional ``two-step'' approach, the ``direct positioning'' approach states that the radio transmitter's position can be extracted directly from the raw samples of the radio transmitter signals collected by the system sensors, without explicitly going through an estimation of position-related parameters such as time-delay, angular or amplitude information. In this work, the concept of direct positioning is applied to various models and consistently outperforms the traditional two-step position estimators, while tightly attaining the theoretical performance bounds. In the sequel, we explore 3 models for radio transmitters and passive target geolocation. The first model discussed in chapter 3, harnesses the transmit signal diversity of MIMO Radar systems to enhance passive-target position estimation via direct estimation algorithms. The algorithms are developed ...

Bar-Shalom, Ofer — Tel-Aviv University


Advanced Multi-Dimensional Signal Processing for Wireless Systems

The thriving development of wireless communications calls for innovative and advanced signal processing techniques targeting at an enhanced performance in terms of reliability, throughput, robustness, efficiency, flexibility, etc.. This thesis addresses such a compelling demand and presents new and intriguing progress towards fulfilling it. We mainly concentrate on two advanced multi-dimensional signal processing challenges for wireless systems that have attracted tremendous research attention in recent years, multi-carrier Multiple-Input Multiple-Output (MIMO) systems and multi-dimensional harmonic retrieval. As the key technologies of wireless communications, the numerous benefits of MIMO and multi-carrier modulation, e.g., boosting the data rate and improving the link reliability, have long been identified and have ignited great research interest. In particular, the Orthogonal Frequency Division Multiplexing (OFDM)-based multi-user MIMO downlink with Space-Division Multiple Access (SDMA) combines the twofold advantages of MIMO and multi-carrier modulation. It is the essential element ...

Cheng, Yao — Ilmenau University of Technology

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