Автор: Mohinder S. Grewal,Angus P. Andrews Название: Kalman Filtering: Theory and Practice with MATLAB ISBN: 1118851218 ISBN-13(EAN): 9781118851210 Издательство: Wiley Рейтинг: Цена: 18050.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The definitive textbook and professional reference on Kalman Filtering fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering.
Автор: Charles K. Chui; Guanrong Chen Название: Kalman Filtering ISBN: 3642099661 ISBN-13(EAN): 9783642099663 Издательство: Springer Рейтинг: Цена: 8487.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method and an indirect method.
Автор: Poularikas Название: Adaptive Filtering ISBN: 1482253356 ISBN-13(EAN): 9781482253351 Издательство: Taylor&Francis Рейтинг: Цена: 15312.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Adaptive filters are used in many diverse applications, appearing in everything from military instruments to cellphones and home appliances. Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB(R) covers the core concepts of this important field, focusing on a vital part of the statistical signal processing area--the least mean square (LMS) adaptive filter.
This largely self-contained text:
Discusses random variables, stochastic processes, vectors, matrices, determinants, discrete random signals, and probability distributions
Explains how to find the eigenvalues and eigenvectors of a matrix and the properties of the error surfaces
Explores the Wiener filter and its practical uses, details the steepest descent method, and develops the Newton's algorithm
Addresses the basics of the LMS adaptive filter algorithm, considers LMS adaptive filter variants, and provides numerous examples
Delivers a concise introduction to MATLAB(R), supplying problems, computer experiments, and more than 110 functions and script files
Featuring robust appendices complete with mathematical tables and formulas, Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB(R) clearly describes the key principles of adaptive filtering and effectively demonstrates how to apply them to solve real-world problems.
Описание: By interrelating concepts and results from system theory with those from econometrics and social sciences, the author has attempted to narrow the gap between the more technical sciences such as engi- neering and social sciences and econometrics, and to contribute to either side.
Автор: Y. Yavin Название: Numerical Studies in Nonlinear Filtering ISBN: 3540139583 ISBN-13(EAN): 9783540139584 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: V.N. Fomin Название: Optimal Filtering ISBN: 0792357345 ISBN-13(EAN): 9780792357346 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This work considers methods for the optimal processing of random fields. In particular, it studies spatio-temporal filtering problems such as the problem of optimal signal detection (Bayes` approach) and estimating angles of arrival of local signals.
Автор: JOSE APOLINARIO JR Название: QRD-RLS Adaptive Filtering ISBN: 1441935266 ISBN-13(EAN): 9781441935267 Издательство: Springer Рейтинг: Цена: 18284.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides tools and knowledge in a simple way so that the reader is able to implement a particular QRD-RLS algorithm tailored for the application at hand. The book comprehensively compiles the research of more than a decade into a single publication.
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.
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