A self-contained approach to DSP techniques and applications in radar imaging
The processing of radar images, in general, consists of three major fields: Digital Signal Processing (DSP); antenna and radar operation; and algorithms used to process the radar images. This book brings together material from these different areas to allow readers to gain a thorough understanding of how radar images are processed.
The book is divided into three main parts and covers:

* DSP principles and signal characteristics in both analog and digital domains, advanced signal sampling, and interpolation techniques
*
Antenna theory (Maxwell equation, radiation field from dipole, and linear phased array), radar fundamentals, radar modulation, and target-detection techniques (continuous wave, pulsed Linear Frequency Modulation, and stepped Frequency Modulation)
*
Properties of radar images, algorithms used for radar image processing, simulation examples, and results of satellite image files processed by Range-Doppler and Stolt interpolation algorithms

The book fully utilizes the computing and graphical capability of MATLAB? to display the signals at various processing stages in 3D and/or cross-sectional views. Additionally, the text is complemented with flowcharts and system block diagrams to aid in readers' comprehension.
Digital Signal Processing Techniques and Applications in Radar Image Processing serves as an ideal textbook for graduate students and practicing engineers who wish to gain firsthand experience in applying DSP principles and technologies to radar imaging.

Dieses Buch gibt eine Einfuhrung in die Nachrichtentechnik bis hin zu modernen Verfahren der Datenubertragung und Datensicherheit. Zu Beginn wird ein Abriss der Geschichte sowie ausgewahlte Modelle der Nachrichtentechnik vorgestellt. Die von Claude E. Shannon begrundete Informationstheorie lost sich von der Bedeutung der Daten und benutzt, vereinfacht ausgedruckt, ausschliesslich deren statistische Eigenschaften. Auf Basis der Informationstheorie werden Einfuhrungen in die Gebiete Quellen- und Kanalcodierung, Ubertragungskanale, Entscheidungstheorie, Modulationsverfahren sowie elementare Kommunikationsprotokolle und Datensicherheit gegeben. Exemplarisch werden zu diesen Gebieten ausgewahlte praktische Verfahren, Methoden und Algorithmen beschrieben. Ein ausfuhrlicher Anhang stellt Grundlagen der Wahrscheinlichkeitsrechnung, der Fourier-Analyse und der Signal- und Systemtheorie bereit."

Achieve faster and more efficient network design and optimization with this comprehensive guide. Some of the most prominent researchers in the field explain the very latest analytic techniques and results from stochastic geometry for modelling the signal-to-interference-plus-noise ratio (SINR) distribution in heterogeneous cellular networks. This book will help readers to understand the effects of combining different system deployment parameters on key performance indicators such as coverage and capacity, enabling the efficient allocation of simulation resources. In addition to covering results for network models based on the Poisson point process, this book presents recent results for when non-Poisson base station configurations appear Poisson, due to random propagation effects such as fading and shadowing, as well as non-Poisson models for base station configurations, with a focus on determinantal point processes and tractable approximation methods. Theoretical results are illustrated with practical Long-Term Evolution (LTE) applications and compared with real-world deployment results.

H Robust design is an advancing technology which aims to achieve the system design purpose under intrinsic random fluctuation and external disturbance. This book introduces several robust design methods, some of which include linear to nonlinear systems and frequency to time domain. This book provides not only a complete theoretical development and application of H robust design over the last three decades, but also an integrated platform for control, signal processing, communication, systems and synthetic biology. Based on the theoretical H robust design results, the authors also give some practical design examples to illustrate the procedure and validate the performance of the proposed H method with computational simulations and tables.

Using easy-to-follow mathematics, this textbook provides comprehensive coverage of block codes and techniques for reliable communications and data storage. It covers major code designs and constructions from geometric, algebraic, and graph-theoretic points of view, decoding algorithms, error control additive white Gaussian noise (AWGN) and erasure, and dataless recovery. It simplifies a highly mathematical subject to a level that can be understood and applied with a minimum background in mathematics, provides step-by-step explanation of all covered topics, both fundamental and advanced, and includes plenty of practical illustrative examples to assist understanding. Numerous homework problems are included to strengthen student comprehension of new and abstract concepts, and a solutions manual is available online for instructors. Modern developments, including polar codes, are also covered. An essential textbook for senior undergraduates and graduates taking introductory coding courses, students taking advanced full-year graduate coding courses, and professionals working on coding for communications and data storage.

Learn about the most recent theoretical and practical advances in radar signal processing using tools and techniques from compressive sensing. Providing a broad perspective that fully demonstrates the impact of these tools, the accessible and tutorial-like chapters cover topics such as clutter rejection, CFAR detection, adaptive beamforming, random arrays for radar, space-time adaptive processing, and MIMO radar. Each chapter includes coverage of theoretical principles, a detailed review of current knowledge, and discussion of key applications, and also highlights the potential benefits of using compressed sensing algorithms. A unified notation and numerous cross-references between chapters make it easy to explore different topics side by side. Written by leading experts from both academia and industry, this is the ideal text for researchers, graduate students and industry professionals working in signal processing and radar.

Presents the Bayesian approach to statistical signal processing for a variety of useful model sets This book aims to give readers a unified Bayesian treatment starting from the basics (Baye s rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on Sequential Bayesian Detection, a new section on Ensemble Kalman Filters as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to fill-in-the gaps of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical sanity testing lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation/detection problems. The second edition of Bayesian Signal Processing features: * Classical Kalman filtering for linear, linearized, and nonlinear systems; modern unscented and ensemble Kalman filters: and the next-generation Bayesian particle filters * Sequential Bayesian detection techniques incorporating model-based schemes for a variety of real-world problems * Practical Bayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics * New case studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving * MATLAB(R) notes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available * Problem sets included to test readers knowledge and help them put their new skills into practice Bayesian Signal Processing, Second Edition is written for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems.

This book focuses on signal processing algorithms based on the timefrequency domain. Original methods and algorithms are presented which are able to extract information from non-stationary signals such as heart sounds and power electric signals. The methods proposed focus on the time-frequency domain, and most notably the Stockwell Transform for the feature extraction process and to identify signatures. For the classification method, the Adaline Neural Network is used and compared with other common classifiers. Theory enhancement, original applications and concrete implementation on FPGA for real-time processing are also covered in this book.

This book grew out of the IEEE-EMBS Summer Schools on Biomedical Signal Processing, which have been held annually since 2002 to provide the participants state-of-the-art knowledge on emerging areas in biomedical engineering. Prominent experts in the areas of biomedical signal processing, biomedical data treatment, medicine, signal processing, system biology, and applied physiology introduce novel techniques and algorithms as well as their clinical or physiological applications.

The book provides an overview of a compelling group of advanced biomedical signal processing techniques, such as multisource and multiscale integration of information for physiology and clinical decision; the impact of advanced methods of signal processing in cardiology and neurology; the integration of signal processing methods with a modelling approach; complexity measurement from biomedical signals; higher order analysis in biomedical signals; advanced methods of signal and data processing in genomics and proteomics; and classification and parameter enhancement.

Digital Signal Processing: Concepts and Applications, second edition covers the basic principles and operation of DSP devices. Its aim is to give the student the essentials of this mathematical subject in a form that can be easily understood and assimilated. The text concentrates on discrete systems, starting from digital filters and discrete Fourier transforms. These are then extended into adaptive filters and spectrum analysers with the minimum of mathematical derivation, concentrating on demonstrating the performance which is achievable from these processors in communications and radar system applications. This new edition has been updated to include learning outcomes and summaries and provide more examples. The text has been completely redesigned and is presented in a clear and easy-to-read style. Key features: * Self assessment questions within the text, with answers provided * Numerous practical worked examples on processor design and performance simulation<* MATLAB (R) code for animated simulations available to students via World Wide Web access This textbook is appropriate for undergraduate and MSc courses in signals and systems and signal processing, and for professional engineers who wish to have a simple, easy-to-read reference book on DSP techniques.

This best-selling, original text focuses on image reconstruction, real-time texture mapping, separable algorithms, two-pass transforms, mesh warping, and special effects. The text, containing all original material, begins with the history of the field and continues with a review of common terminology, mathematical preliminaries, and digital image acquisition. Later chapters discuss equations for spatial information, interpolation kernels, filtering problems, and fast-warping techniques based on scanline algorithms.

This textbook provides comprehensive coverage for courses in the basics of design and implementation of digital filters. The book assumes only basic knowledge in digital signal processing and covers state-of-the-art methods for digital filter design and provides a simple route for the readers to design their own filters. The advanced mathematics that is required for the filter design is minimized by providing an extensive MATLAB toolbox with over 300 files. The book presents over 200 design examples with MATLAB code and over 300 problems to be solved by the reader. The students can design and modify the code for their use. The book and the design examples cover almost all known design methods of frequency-selective digital filters as well as some of the authors' own, unique techniques.

While previous EW exploited flaws in the analogue equipment to corrupt or degrade the sensor detection or localisation capabilities, EW is now an information battle. Modern autonomous threat sensors can readily detect and locate targets by incorporating state of the art high speed digital signal processing (DSP) algorithms that focus on the classification of targets via target physical features. As a result the autonomous threat has a significant advantage over attacking forces consisting of armoured vehicles, aircraft or ships. To elucidate the state of EW, this book focuses on the example of autonomous anti ship missiles (ASM) attacking a naval fleet rather than airborne battles, thus filling a significant gap in the EW literature. It describes modern DSP algorithms that have been published by ASM development personnel from several nations, including the People's Republic of China and the Russian federation and outlines instances where it has been successfully used against ships. The book elaborates on the mathematical techniques employed and the advantages of incorporating digital signal processing algorithms into the autonomous sensor. With straight forward DSP algorithms, ASM can rapidly identify and distinguish electronically generated false targets, passive decoys, chaff and true targets. Moreover, special sensor waveforms now proactively probe the targets for enhanced feature measurements, and modern multi-channel optimal DSP readily mitigates noise jamming.

Exploring the interrelation between information theory and signal processing theory, the book contains a new algebraic approach to signal processing theory. Readers will learn this new approach to constructing the unified mathematical fundamentals of both information theory and signal processing theory in addition to new methods of evaluating quality indices of signal processing. The book discusses the methodology of synthesis and analysis of signal processing algorithms providing qualitative increase of signal processing efficiency under parametric and nonparametric prior uncertainty conditions. Examples are included throughout the book to further emphasize new material.

A comprehensive guide to restoring images degraded by motion blur, bridging the traditional approaches and emerging computational photography-based techniques, and bringing together a wide range of methods emerging from basic theory as well as cutting-edge research. It encompasses both algorithms and architectures, providing detailed coverage of practical techniques by leading researchers. From an algorithms perspective, blind and non-blind approaches are discussed, including the use of single or multiple images; projective motion blur model; image priors and parametric models; high dynamic range imaging in the irradiance domain; and image recognition in blur. Performance limits for motion deblurring cameras are also presented. From a systems perspective, hybrid frameworks combining low-resolution-high-speed and high-resolution-low-speed cameras are described, along with the use of inertial sensors and coded exposure cameras. Also covered is an architecture exploiting compressive sensing for video recovery. A valuable resource for researchers and practitioners in computer vision, image processing, and related fields.

The field of signal processing has seen exponential growth during the past decade, as remarkable innovations both in research and application have been made. The applications of signal processing are numerous and include audio signal processing, biomedical engineering, multimedia, video signal processing, pattern analysis, pattern recognition, artificial intelligence, decision making, control systems, and many more. In the past few years, a new wave of advanced signal-processing techniques has delivered exciting results, increasing systems capabilities of efficiently exchanging data and extracting useful knowledge from them. The theory and applications of signal processing are concerned with the identification, modelling and utilisation of patterns and structures in a signal process. This book is aimed to provide a self-contained introduction to the subject as well as offering a set of invited contributions, which we see as lying at the cutting edge of both empirical and computational aspects of signal processing research. This book was born from discussions with researchers in the signal processing community and aims to provide a snapshot of some current trends and future challenges in signal processing research. This book presents state-of-the-art and recent research results on the application of advanced signal processing techniques for improving the value of signal, image and video data. The book is likely to be of interest to post graduate students, researchers, engineers, and professors - in the field of signal processing. This book is organized into 13 chapters, covering diverse applications of signal processing research.