Restoration of DWI Data Using a Rician LMMSE Estimator

This paper introduces and analyzes a linear minimum mean square error (LMMSE) estimator using a Rician noise model and its recursive version (RLMMSE) for the restoration of diffusion weighted images. A method to estimate the noise level based on local estimations of mean or variance is used to automatically parametrize the estimator. The restoration performance is evaluated using quality indexes and compared to alternative estimation schemes. The overall scheme is simple, robust, fast, and improves estimations. Filtering diffusion weighted magnetic resonance imaging (DW-MRI) with the proposed methodology leads to more accurate tensor estimations. Real and synthetic datasets are analyzed

Modeling the statistics of high resolution SAR images

In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for modelling the statistics of intensities in high resolution Synthetic Aperture Radar (SAR) images. Along with the models we design an efficient parameter estimation scheme by integrating the Stochastic Expectation Maximization scheme and the Method of log-cumulants with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). In particular, the proposed dictionary consists of eight most efficient state-of-the-art SAR-specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The experiment results with a set of several real SAR (COSMO-SkyMed) images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive measures such as correlation coefficient (always above 99,5%) . We stress, in particular, that the method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous images

Modeling the statistics of high resolution SAR images

In the context of remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of pixel intensities. In this work, we develop a parametric finite mixture model for modelling the statistics of intensities in high resolution Synthetic Aperture Radar (SAR) images. Along with the models we design an efficient parameter estimation scheme by integrating the Stochastic Expectation Maximization scheme and the Method of log-cumulants with an automatic technique to select, for each mixture component, an optimal parametric model taken from a predefined dictionary of parametric probability density functions (pdf). In particular, the proposed dictionary consists of eight most efficient state-of-the-art SAR-specific pdfs: Nakagami, log-normal, generalized Gaussian Rayleigh, Heavy-tailed Rayleigh, Weibull, K-root, Fisher and generalized Gamma. The experiment results with a set of several real SAR (COSMO-SkyMed) images demonstrate the high accuracy of the designed algorithm, both from the viewpoint of a visual comparison of the histograms, and from the viewpoint of quantitive measures such as correlation coefficient (always above 99,5%) . We stress, in particular, that the method proves to be effective on all the considered images, remaining accurate for multimodal and highly heterogeneous images

An Algorithm for Automatic Target Recognition Using Passive Radar and an EKF for Estimating Aircraft Orientation

Rather than emitting pulses, passive radar systems rely on illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. These systems are attractive since they allow receivers to operate without emitting energy, rendering them covert. Until recently, most of the research regarding passive radar has focused on detecting and tracking targets. This dissertation focuses on extending the capabilities of passive radar systems to include automatic target recognition. The target recognition algorithm described in this dissertation uses the radar cross section (RCS) of potential targets, collected over a short period of time, as the key information for target recognition. To make the simulated RCS as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. An extended Kalman filter (EKF) estimates the target's orientation (and uncertainty in the estimate) from velocity measurements obtained from the passive radar tracker. Coupling the aircraft orientation and state with the known antenna locations permits computation of the incident and observed azimuth and elevation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of potential target classes as a function of these angles. Thus, the approximated incident and observed angles allow the appropriate RCS to be extracted from a database of FISC results. Using this process, the RCS of each aircraft in the target class is simulated as though each is executing the same maneuver as the target detected by the system. Two additional scaling processes are required to transform the RCS into a power profile (magnitude only) simulating the signal in the receiver. First, the RCS is scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. Then, the Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern, further scaling the RCS. A Rician likelihood model compares the scaled RCS of the illuminated aircraft with those of the potential targets. To improve the robustness of the result, the algorithm jointly optimizes over feasible orientation profiles and target types via dynamic programming.Ph.D.Committee Chair: Lanterman, Aaron; Committee Member: McLaughlin, Steve; Committee Member: Richards, Mark; Committee Member: Serban, Nicoleta; Committee Member: Verriest, Eri

Design and theoretical analysis of advanced power based positioning in RF system

Accurate locating and tracking of people and resources has become a fundamental requirement for many applications. The global navigation satellite systems (GNSS) is widely used. But its accuracy suffers from signal obstruction by buildings, multipath fading, and disruption due to jamming and spoof. Hence, it is required to supplement GPS with inertial sensors and indoor localization schemes that make use of WiFi APs or beacon nodes. In the GPS-challenging or fault scenario, radio-frequency (RF) infrastructure based localization schemes can be a fallback solution for robust navigation. For the indoor/outdoor transition scenario, we propose hypothesis test based fusion method to integrate multi-modal localization sensors. In the first paper, a ubiquitous tracking using motion and location sensor (UTMLS) is proposed. As a fallback approach, power-based schemes are cost-effective when compared with the existing ToA or AoA schemes. However, traditional power-based positioning methods suffer from low accuracy and are vulnerable to environmental fading. Also, the expected accuracy of power-based localization is not well understood but is needed to derive the hypothesis test for the fusion scheme. Hence, in paper 2-5, we focus on developing more accurate power-based localization schemes. The second paper improves the power-based range estimation accuracy by estimating the LoS component. The ranging error model in fading channel is derived. The third paper introduces the LoS-based positioning method with corresponding theoretical limits and error models. In the fourth and fifth paper, a novel antenna radiation-pattern-aware power-based positioning (ARPAP) system and power contour circle fitting (PCCF) algorithm are proposed to address antenna directivity effect on power-based localization. Overall, a complete LoS signal power based positioning system has been developed that can be included in the fusion scheme --Abstract, page iv

Contribuições analíticas para sistemas de radar modernos

Orientador: José Cândido Silveira Santos FilhoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Esta tese tem como objetivo avançar no campo de sistemas de radar ao lidar com os seguintes problemas centrais: (i) detecção de alvos distribuídos e pontuais imersos em ruído Gaussiano branco complexo; (ii) desempenho de sistemas de radar na presença de clutter terrestre do tipo Weibull; e (iii) estimação Doppler para alvos de alta velocidade sob ruído Gaussiano de fundo. A primeira parte da tese (Capítulos 2-4) ataca o primeiro problema, por meio do projeto e da análise de detectores phased array ótimos e subótimos para alvos distribuídos e alvos pontuais não-flutuantes. Para cada detector, as estatísticas da variável de decisão são analisadas sob a hipótese de algum - ou mesmo nenhum - conhecimento acerca dos parâmetros do alvo e da potência média do ruído. A partir daí, calculam-se a probabilidade de detecção e a probabilidade de falso alarme. A segunda parte da tese (Capítulos 5 e 6) confronta o segundo problema, fornecendo ferramentas matemáticas eficientes para avaliar o desempenho de um detector square-law operando em clutter terrestre do tipo Weibull. Aqui, as probabilidades de detecção e falso alarme são obtidas em forma fechada e em representação por séries de convergência rápida. Para isso, faz-se uso da função-H de Fox, bem como de um cálculo abrangente de resíduos. Finalmente, na terceira parte da tese (Capítulo 7), é fornecida uma análise estatística completa da estimação Doppler de alvos com alta velocidade sujeitos a ruído Gaussiano de fundo. A solução apresentada combina duas técnicas de processamento de sinais: o processamento de subpulso e o Teorema Chinês do Resto clássico. Além disso, o desempenho dessa técnica híbrida é avaliado em forma fechada. Vale ressaltar que todas as expressões supracitadas da tese são contribuições originais, com destaque para aquelas obtidas em representações por série, que se mostram atrativas pela ampla economia tanto de tempo de execução quanto de carga computacionalAbstract: This dissertation aims to advance in the field of radar systems by dealing with the following key problems: (i) detection of distributed and point-like targets embedded in complex white Gaussian noise; (ii) radar performance in the presence of Weibull-distributed ground clutter; and (iii) doppler estimation for high-velocity targets in background Gaussian noise. The first part of this dissertation (Chapters 2-4) addresses the first problem by designing and analyzing optimal and suboptimal phased-array detectors for distributed and non-fluctuating point-like targets. For each detector, the decision-variable statistics are investigated assuming a certain or no knowledge about the parameters of the target echoes and the average noise power. In each case, the probability of detection and the probability of false alarm are derived. The second part of this dissertation (Chapters 5 and 6) addresses the second problem by providing efficient mathematical tools to evaluate the performance of a square-law detector operating in Weibull-distributed ground clutter. In this case, the probabilities of detection and false alarm are expressed in terms of both closed-form expressions and fast convergent series. To do so, we rely upon the Fox H-function as well as a comprehensive calculus of residues. Finally, in the third part of this dissertation (Chapter 7), we provide a thorough statistical analysis for the Doppler estimation of high-speed targets in background Gaussian noise. The proposed solution combines two signal processing techniques: subpulse processing and the classic Chinese Remainder Theorem. Also, the performance of this hybrid technique is assessed in closed form. It is worth mentioning that all the aforementioned expressions from this dissertation are original contributions, with emphasis on those obtained in terms of series representations, which proved attractive for large savings in both execution time and computational loadDoutoradoTelecomunicações e TelemáticaCAPE

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin