Chaotic Dynamics of Sea Clutter


Book Description

Chaotic dynamics of sea clutter boasts important applications in a number of fields, including weather radar systems, which are intensely studied at major universities. This book presents a detailed account of chaotic dynamics of sea clutter, using real-life sea clutter data rather than simulated data, and drawing on eight years of research by one of the most highly regarded researchers in this area.




Chaotic Dynamics of Sea Clutter


Book Description




Chaotic Signal Processing


Book Description

Chaos is a deterministic random phenomenon. Many signal processes (e.g., radar and sonar) have a random appearance, and chaos provides an alternative approach to processing these signals. This book presents up-to-date research results on chaotic signal processing, including the application of nonlinear dynamics to radar target recognition, an exactly solvable chaos approach for communications, a chaotic approach for reconfigurable computing, system identification using chaos, design of a high resolution LADAR system based on chaos, and the use of chaos in compressive sensing.




Sea Clutter


Book Description

Sea Clutter: Scattering, the K Distribution and Radar Performance examines the statistics of radar scattering from the sea surface in terms of their relevance to radar operating in a maritime environment; including remote sensing, surveillance and targeting applications. A lot of the work in the book is based on the compound Kdistribution model for the amplitude statistics of sea clutter. In addition, the book addresses the specification of performance required by customers and the measurement of performance of systems supplied to customers.







Reconstruction of Chaotic Signals with Applications to Chaos-based Communications


Book Description

This book provides a systematic review of the fundamental theory of signal reconstruction and the practical techniques used in reconstructing chaotic signals. Specific applications of signal reconstruction methods in chaos-based communications are expounded in full detail, along with examples illustrating the various problems associated with such applications.The book serves as an advanced textbook for undergraduate and graduate courses in electronic and information engineering, automatic control, physics and applied mathematics. It is also highly suited for general nonlinear scientists who wish to understand the basics of chaos-based signal and information processing. Written with numerous illustrative applications to capture the interest of casual readers, the book also contains adequate theoretical rigor to provide the necessary foundational as well as advanced material for serious researchers who are working or aspire to work in this area.




Adaptive Radar Signal Processing


Book Description

This collaborative work presents the results of over twenty years of pioneering research by Professor Simon Haykin and his colleagues, dealing with the use of adaptive radar signal processing to account for the nonstationary nature of the environment. These results have profound implications for defense-related signal processing and remote sensing. References are provided in each chapter guiding the reader to the original research on which this book is based.




Kalman Filtering and Neural Networks


Book Description

State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover: An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF) Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm The unscented Kalman filter Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.




Communications, Signal Processing, and Systems


Book Description

This book brings together papers presented at the 2020 International Conference on Communications, Signal Processing, and Systems, which provides a venue to disseminate the latest developments and to discuss the interactions and links between these multidisciplinary fields. Spanning topics ranging from communications, signal processing and systems, this book is aimed at undergraduate and graduate students in Electrical Engineering, Computer Science and Mathematics, researchers and engineers from academia and industry as well as government employees (such as NSF, DOD and DOE).




Recent Developments in Cooperative Control and Optimization


Book Description

Over the past several years, cooperative control and optimization has un questionably been established as one of the most important areas of research in the military sciences. Even so, cooperative control and optimization tran scends the military in its scope -having become quite relevant to a broad class of systems with many exciting, commercial, applications. One reason for all the excitement is that research has been so incredibly diverse -spanning many scientific and engineering disciplines. This latest volume in the Cooperative Systems book series clearly illustrates this trend towards diversity and creative thought. And no wonder, cooperative systems are among the hardest systems control science has endeavored to study, hence creative approaches to model ing, analysis, and synthesis are a must! The definition of cooperation itself is a slippery issue. As you will see in this and previous volumes, cooperation has been cast into many different roles and therefore has assumed many diverse meanings. Perhaps the most we can say which unites these disparate concepts is that cooperation (1) requires more than one entity, (2) the entities must have some dynamic behavior that influences the decision space, (3) the entities share at least one common objective, and (4) entities are able to share information about themselves and their environment. Optimization and control have long been active fields of research in engi neering.