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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...

While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields. This book brings together cutting-edge information previously available only in disparate papers and articles, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind channel...

Handbook on Array Processing and Sensor Networks provides readers with a collection of tutorial articles contributed by world-renowned experts on recent advancements and the state of the art in array processing and sensor networks.

Focusing on fundamental principles as well as applications, the handbook provides exhaustive coverage of: wavelets; spatial spectrum estimation; MIMO radio propagation; robustness issues in sensor array processing; wireless communications and...

This book presents cutting-edge research in wireless communications, particularly in the fast-growing subject of multiple-input multiple-output (MIMO) wireless communication systems. It begins with an introduction, which includes historical notes and a review of turbo-information processing and MIMO wireless communications, and goes on to cover:

• MIMO channel capacity

• BLAST architectures

• Space-time turbo codes and turbo decoding principles

• Turbo-BLAST

...

This book fills the gap between the literature on nonlinear filters and nonlinear observers by presenting a new state estimation strategy, the smooth variable structure filter (SVSF). The book is a valuable resource to researchers outside of the control society, where literature on nonlinear observers is less well-known. SVSF is a predictor-corrector estimator that is formulated based on a stability theorem, to confine the estimated states within a neighborhood of their true values. It has the potential to improve performance in the presence of severe and changing modeling uncertainties...

• Edited by the original inventor of the technology.
• Includes contributions by the foremost experts in the field.
• The only book to cover these topics together.