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


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


Sparse Signal Recovery From Incomplete And Perturbed Data

Sparse signal recovery consists of algorithms that are able to recover undersampled high dimensional signals accurately. These algorithms require fewer measurements than traditional Shannon/Nyquist sampling theorem demands. Sparse signal recovery has found many applications including magnetic resonance imaging, electromagnetic inverse scattering, radar/sonar imaging, seismic data collection, sensor array processing and channel estimation. The focus of this thesis is on electromagentic inverse scattering problem and joint estimation of the frequency offset and the channel impulse response in OFDM. In the electromagnetic inverse scattering problem, the aim is to find the electromagnetic properties of unknown targets from measured scattered field. The reconstruction of closely placed point-like objects is investigated. The application of the greedy pursuit based sparse recovery methods, OMP and FTB-OMP, is proposed for increasing the reconstruction resolution. The performances of the proposed methods are compared against NESTA and MT-BCS methods. ...

Senyuva, Rifat Volkan — Bogazici University


Estimation for Sensor Fusion and Sparse Signal Processing

Progressive developments in computing and sensor technologies during the past decades have enabled the formulation of increasingly advanced problems in statistical inference and signal processing. The thesis is concerned with statistical estimation methods, and is divided into three parts with focus on two different areas: sensor fusion and sparse signal processing. The first part introduces the well-established Bayesian, Fisherian and least-squares estimation frameworks, and derives new estimators. Specifically, the Bayesian framework is applied in two different classes of estimation problems: scenarios in which (i) the signal covariances themselves are subject to uncertainties, and (ii) distance bounds are used as side information. Applications include localization, tracking and channel estimation. The second part is concerned with the extraction of useful information from multiple sensors by exploiting their joint properties. Two sensor configurations are considered here: (i) a monocular camera and an inertial ...

Zachariah, Dave — KTH Royal Institute of Technology


Sparse Sensing for Statistical Inference: Theory, Algorithms, and Applications

In today's society, we are flooded with massive volumes of data in the order of a billion gigabytes on a daily basis from pervasive sensors. It is becoming increasingly challenging to locally store and transport the acquired data to a central location for signal/data processing (i.e., for inference). To alleviate these problems, it is evident that there is an urgent need to significantly reduce the sensing cost (i.e., the number of expensive sensors) as well as the related memory and bandwidth requirements by developing unconventional sensing mechanisms to extract as much information as possible yet collecting fewer data. The first aim of this thesis is to develop theory and algorithms for data reduction. We develop a data reduction tool called sparse sensing, which consists of a deterministic and structured sensing function (guided by a sparse vector) that is optimally designed ...

Chepuri, Sundeep Prabhakar — Delft University of Technology


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


Theoretical aspects and real issues in an integrated multiradar system

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 ...

Fortunati Stefano — University of Pisa


Wideband Data-Independent Beamforming for Subarrays

The desire to operate large antenna arrays for e.g. RADAR applications over a wider frequency range is currently limited by the hardware, which due to weight, cost and size only permits complex multipliers behind each element. In contrast, wideband processing would have to rely on tap delay lines enabling digital filters for every element. As an intermediate step, in this thesis we consider a design where elements are grouped into subarrays, within which elements are still individually controlled by narrowband complex weights, but where each subarray output is given a tap delay line or finite impulse response digital filter for further wideband processing. Firstly, this thesis explores how a tap delay line attached to every subarray can be designed as a delay-and-sum beamformer. This filter is set to realised a fractional delay design based on a windowed sinc function. At ...

Alshammary, Abdullah — University of Strathclyde


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 R´enyi entropy as the dissimilarity metric between images. Since the R´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


Separability of Closely Spaced Users in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) evolved to a key enabling physical layer (PHY) technology for the fifth generation (5G) of mobile communication systems and beyond. While the envisioned use cases of such communications systems are diverse, so are the challenges to meet their respective requirements. As a large-scale evolution of already well-established MIMO communications technologies, massive MIMO promises benefits with respect to all possible use cases. Theoretical works on massive MIMO, however, typically assumes i.i.d. Rayleigh fading channels without spatial channel correlation. The application of this model is justified through the assumption of rich scattering environments, which is claimed to hold, for example, in indoor environments. Spatial correlation of wireless channels leads to inter-stream interference in single-user MIMO communications systems and to inter-user interference in multi-user MIMO systems with linear precoding. Channel correlation is therefore crucial for the performance of such ...

Stefan Pratschner — TU Wien


Advanced Signal Processing Concepts for Multi-Dimensional Communication Systems

The widespread use of mobile internet and smart applications has led to an explosive growth in mobile data traffic. With the rise of smart homes, smart buildings, and smart cities, this demand is ever growing since future communication systems will require the integration of multiple networks serving diverse sectors, domains and applications, such as multimedia, virtual or augmented reality, machine-to-machine (M2M) communication / the Internet of things (IoT), automotive applications, and many more. Therefore, in the future, the communication systems will not only be required to provide Gbps wireless connectivity but also fulfill other requirements such as low latency and massive machine type connectivity while ensuring the quality of service. Without significant technological advances to increase the system capacity, the existing telecommunications infrastructure will be unable to support these multi-dimensional requirements. This poses an important demand for suitable waveforms with ...

Cheema, Sher Ali — Technische Universität Ilmenau


Signal processing of FMCW Synthetic Aperture Radar data

In the field of airborne earth observation there is special attention to compact, cost effective, high resolution imaging sensors. Such sensors are foreseen to play an important role in small-scale remote sensing applications, such as the monitoring of dikes, watercourses, or highways. Furthermore, such sensors are of military interest; reconnaissance tasks could be performed with small unmanned aerial vehicles (UAVs), reducing in this way the risk for one's own troops. In order to be operated from small, even unmanned, aircrafts, such systems must consume little power and be small enough to fulfill the usually strict payload requirements. Moreover, to be of interest for the civil market, cost effectiveness is mandatory. Frequency Modulated Continuous Wave (FMCW) radar systems are generally compact and relatively cheap to purchase and to exploit. They consume little power and, due to the fact that they are ...

Meta, Adriano — Delft University of Technology


Analysis, Modelling, and Simulation of an Integrated Multisensor System for Maritime Border Control

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, ...

Giompapa, Sofia — Universita di Pisa


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


Pointwise shape-adaptive DCT image filtering and signal-dependent noise estimation

When an image is acquired by a digital imaging sensor, it is always degraded by some noise. This leads to two basic questions: What are the main characteristics of this noise? How to remove it? These questions in turn correspond to two key problems in signal processing: noise estimation and noise removal (so-called denoising). This thesis addresses both abovementioned problems and provides a number of original and effective contributions for their solution. The first part of the thesis introduces a novel image denoising algorithm based on the low-complexity Shape-Adaptive Discrete Cosine Transform (SA-DCT). By using spatially adaptive supports for the transform, the quality of the filtered image is high, with clean edges and without disturbing artifacts. We further present extensions of this approach to image deblurring, deringing and deblocking, as well as to color image filtering. For all these applications, ...

Foi, Alessandro — Tampere University of Technology

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