Kalman Filtering: Theory and Practice with MATLAB, Mohinder S. Grewal,Angus P. Andrews
Старое издание
Автор: Grewal Название: Kalman Filtering: Theory and Practice Using MATLAB, 3rd Edition ISBN: 0470173661 ISBN-13(EAN): 9780470173664 Издательство: Wiley Цена: 14018.00 р. Наличие на складе: Невозможна поставка. Описание: Organized for use as a text for an introductory course in stochastic processes at the senior level and as a first-year, graduate-level course in Kalman filtering theory and applications, this book includes real-world problems in practice as illustrative examples, and also covers the more practical aspects of implementation. The author Grewal teaches at Cal State Fullerton and also offers seminars and tutorials on Kalman Filters. Dr. Grewal has contributed the Article on Kalman Filters for the Webster Encyclopedia.
Описание: This book presents a unique viewpoint of signal processing from the Bayesian perspective in contrast to the pure statistical approach found in many textbooks. It features the next generation of processors that have recently been enabled with the advent of high speed/high throughput computers. The emphasis is on nonlinear/non-Gaussian problems, but classical techniques are included as special cases to enable the reader familiar with such methods to draw a parallel between the approaches. The common ground is the model sets. This text brings the reader from the classical methods of model-based signal processing including Kalman filtering for linear, linearized and approximate nonlinear processors as well as the recently developed unscented or sigma-point filters to the next generation of processors that will clearly dominate the future of model-based signal processing for years to come. Current applications (e.g. structures, tracking, equalization, biomedical) and simple examples to motivate the organization of the text are discussed. Examples are given to motivate all of the models and prepare the reader for further developments in subsequent chapters. In each case the processor along with accompanying simulations are discussed and applied to various data sets demonstrating the applicability and power of the Bayesian approach. The proposed text will be linked to the MATLAB (signal processing standard software) software package providing Notes as well as simple coding examples for illustrative purposes.
Описание: Subband adaptive filtering is rapidly becoming one of the most effective techniques for reducing computational complexity and improving the convergence rate of algorithms in adaptive signal processing applications. This book provides an introductory, yet extensive guide on the theory of various subband adaptive filtering techniques.
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