Download Free Advanced Concepts In Adaptive Signal Processing Book in PDF and EPUB Free Download. You can read online Advanced Concepts In Adaptive Signal Processing and write the review.

Although adaptive filtering and adaptive array processing began with research and development efforts in the late 1950's and early 1960's, it was not until the publication of the pioneering books by Honig and Messerschmitt in 1984 and Widrow and Stearns in 1985 that the field of adaptive signal processing began to emerge as a distinct discipline in its own right. Since 1984 many new books have been published on adaptive signal processing, which serve to define what we will refer to throughout this book as conventional adaptive signal processing. These books deal primarily with basic architectures and algorithms for adaptive filtering and adaptive array processing, with many of them emphasizing practical applications. Most of the existing textbooks on adaptive signal processing focus on finite impulse response (FIR) filter structures that are trained with strategies based on steepest descent optimization, or more precisely, the least mean square (LMS) approximation to steepest descent. While literally hundreds of archival research papers have been published that deal with more advanced adaptive filtering concepts, none of the current books attempt to treat these advanced concepts in a unified framework. The goal of this new book is to present a number of important, but not so well known, topics that currently exist scattered in the research literature. The book also documents some new results that have been conceived and developed through research conducted at the University of Illinois during the past five years.
This book is an accessible guide to adaptive signal processing methods that equips the reader with advanced theoretical and practical tools for the study and development of circuit structures and provides robust algorithms relevant to a wide variety of application scenarios. Examples include multimodal and multimedia communications, the biological and biomedical fields, economic models, environmental sciences, acoustics, telecommunications, remote sensing, monitoring and in general, the modeling and prediction of complex physical phenomena. The reader will learn not only how to design and implement the algorithms but also how to evaluate their performance for specific applications utilizing the tools provided. While using a simple mathematical language, the employed approach is very rigorous. The text will be of value both for research purposes and for courses of study.
The creation of the text really began in 1976 with the author being involved with a group of researchers at Stanford University and the Naval Ocean Systems Center, San Diego. At that time, adaptive techniques were more laboratory (and mental) curiosities than the accepted and pervasive categories of signal processing that they have become. Over the lasl 10 years, adaptive filters have become standard components in telephony, data communications, and signal detection and tracking systems. Their use and consumer acceptance will undoubtedly only increase in the future. The mathematical principles underlying adaptive signal processing were initially fascinating and were my first experience in seeing applied mathematics work for a paycheck. Since that time, the application of even more advanced mathematical techniques have kept the area of adaptive signal processing as exciting as those initial days. The text seeks to be a bridge between the open literature in the professional journals, which is usually quite concentrated, concise, and advanced, and the graduate classroom and research environment where underlying principles are often more important.
Teaches students about classical and nonclassical adaptive systems within one pair of covers Helps tutors with time-saving course plans, ready-made practical assignments and examination guidance The recently developed "practical sub-space adaptive filter" allows the reader to combine any set of classical and/or non-classical adaptive systems to form a powerful technology for solving complex nonlinear problems
The book addresses the current demand for a scientific approach to advanced wireless technology and its future developments. It gives a clear presentation of both antennas and adaptive signal processing which is what makes antennas powerful, maneuverable and necessary for advanced wireless technology. The book presents electromagnetic signal processing techniques that both control the antenna beam and track the moving station, which is required for effective, fast, dynamic beamforming. The first part of the book presents a comprehensive description and analysis of basic antenna theory, starting from short dipole antennas to array antennas. This section also includes important concepts related to antenna parameters, electromagnetic wave propagation, the Friis equation, the radar equation and wave reflection and transmission through media. The second part of the book focuses on smart antennas, commencing from a look at the traditional approach to beamforming before getting into the details of smart antennas. Complete derivation and description of the techniques for electromagnetic field signal processing techniques for adaptive beamforming are also presented. Artificial Intelligence (AI) driven beamforming is presented using computationally fast and low-memory demanding technique for AI beamforming is presented with the different excitation functions available. A novel method for fast, low memory and accurate, maneuverable single beam generation is presented, as well as other methods for beamforming with fewer elements along with a simple method for tracking the mobile antenna and station. In this section, for completeness, the use of antenna signal processing for synthetic aperture techniques for imaging is also presented, specifically the Inverse Synthetic Aperture Imaging technique. The third part of the book presents technological aspects of advanced wireless technology, including the 5G wireless system and the various devices needed to construct it. While the books’ main emphasis is theoretical understanding and design, it includes applications, and legal matters are also presented.
A valuable resource for radar engineers and managers of all levels, this revised edition provides an introduction to the capabilities and limitations of radar, as well as a detailed advanced study of key radar signal processing topics. The book explains the concepts and theory of radar signal processing such as resolution, ambiguities, antennas, waveforms, the theory of detecting targets in noise and/or clutter, and tracking using data processing. It also presents equations for the determination of maximum radar range in free space and as affected by multipath and the horizon.
Advances in digital signal processing algorithms and computer technology have combined to produce real-time systems with capabilities far beyond those of just few years ago. Nonlinear, adaptive methods for signal processing have emerged to provide better array gain performance, however, they lack the robustness of conventional algorithms. The chall
Discover the Applicability, Benefits, and Potential of New Technologies As advances in algorithms and computer technology have bolstered the digital signal processing capabilities of real-time sonar, radar, and non-invasive medical diagnostics systems, cutting-edge military and defense research has established conceptual similarities in these areas. Now civilian enterprises can use government innovations to facilitate optimal functionality of complex real-time systems. Advanced Signal Processing details a cost-efficient generic processing structure that exploits these commonalities to benefit commercial applications. Learn from a Renowned Defense Scientist, Researcher, and Innovator The author preserves the mathematical focus and key information from the first edition that provided invaluable coverage of topics including adaptive systems, advanced beamformers, and volume visualization methods in medicine. Integrating the best features of non-linear and conventional algorithms and explaining their application in PC-based architectures, this text contains new data on: Advances in biometrics, image segmentation, registration, and fusion techniques for 3D/4D ultrasound, CT, and MRI Fully digital 3D/ (4D: 3D+time) ultrasound system technology, computing architecture requirements, and relevant implementation issues State-of-the-art non-invasive medical procedures, non-destructive 3D tomography imaging and biometrics, and monitoring of vital signs Cardiac motion correction in multi-slice X-ray CT imaging Space-time adaptive processing and detection of targets interference-intense backgrounds comprised of clutter and jamming With its detailed explanation of adaptive, synthetic-aperture, and fusion-processing schemes with near-instantaneous convergence in 2-D and 3-D sensors (including planar, circular, cylindrical, and spherical arrays), the quality and illustration of this text’s concepts and techniques will make it a favored reference.
Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters. Covers the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, and the extended kernel recursive least squares algorithm Presents a powerful model-selection method called maximum marginal likelihood Addresses the principal bottleneck of kernel adaptive filters—their growing structure Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site Concludes each chapter with a summary of the state of the art and potential future directions for original research Kernel Adaptive Filtering is ideal for engineers, computer scientists, and graduate students interested in nonlinear adaptive systems for online applications (applications where the data stream arrives one sample at a time and incremental optimal solutions are desirable). It is also a useful guide for those who look for nonlinear adaptive filtering methodologies to solve practical problems.