Adaptive Filtering

Implicit filtering is a way to solve bound-constrained optimization problems for which derivative information is not available. This book describes the algorithm, its convergence theory and a new MATL

The first monograph devoted exclusively to spatial filtering velocimetry, this book includes fundamental theory, imaging optics, signal analysis, spatial filtering devices and systems, plus applicatio

The book covers control and filtering problems for several classes of stochastic networked systems including stability, robustness, reliability, consensus performance, and/or disturbance attenuation l

This text for advanced undergraduates and graduate students provides a concise introduction to increasingly important topics in electrical engineering: digital filtering, filter design, and applicatio

Adaptive Sensory Environments: An Introduction presents a cutting-edge methodology for adaptive sensory design by fostering an inter-disciplinary approach in which aspects of neuroscience, biophilia,

Adaptive Sensory Environments: An Introduction presents a cutting-edge methodology for adaptive sensory design by fostering an inter-disciplinary approach in which aspects of neuroscience, biophilia,

Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median f

This book presents a comprehensive and novel adaptive enterprise service systems approach to adapting, defining, operating, managing and supporting (ADOMS) the adaptive cloud enterprise architecture.

This book is designed to provide an ideal introduction to the field of adaptive optics. In the past few years, there has been constant development in adaptive optics technology, theory, and systems gr

Reflecting the state of the art in adaptive design approaches, Adaptive Design Theory and Implementation Using SAS and R provides a concise, unified presentation of adaptive design theories, uses SAS

Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book's practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include Matlab computations, and the numerous end-of-chapter exercises include

Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book's practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include Matlab computations, and the numerous end-of-chapter exercises include

Model Free Adaptive Control (MFAC) is a novel data driven adaptive control method which is quite different from the traditional model based adaptive control. Traditional control methods are applied ba

An adaptive system for linear systems with unknown parameters is a nonlinear system. The analysis of such adaptive systems requires similar techniques to analysis for nonlinear systems. Therefore it i

Linear Systems: Non-Fragile Control and Filtering presents the latest research results and a systematic approach to designing non-fragile controllers and filters for linear systems. The authors combin

Provides an intrduction to the control and filtering algorithims devised for distributed, interconnected environments with a particular emphasis on industrial applications.

The estimation of noisily observed states from a sequence of data has traditionally incorporated ideas from Hilbert spaces and calculus-based probability theory. As conditional expectation is the key concept, the correct setting for filtering theory is that of a probability space. Graduate engineers, mathematicians and those working in quantitative finance wishing to use filtering techniques will find in the first half of this book an accessible introduction to measure theory, stochastic calculus, and stochastic processes, with particular emphasis on martingales and Brownian motion. Exercises are included. The book then provides an excellent users' guide to filtering: basic theory is followed by a thorough treatment of Kalman filtering, including recent results which extend the Kalman filter to provide parameter estimates. These ideas are then applied to problems arising in finance, genetics and population modelling in three separate chapters, making this a comprehensive resource for b

The arrival of the computer in educational and psychological testing has boosted the popularity of adaptive testing. This book covers key features of adaptive testing and shows how these features are

Presents readers with the basic science, technology, and applications for every type of adaptive lensAn adaptive lens is a lens whose shape has been changed to a different focal length by an external

The Fourth Edition to the Introduction of Random Signals and Applied Kalman Filtering is updated to cover innovations in the Kalman filter algorithm and the proliferation of Kalman filtering applicati

This book offers an analytical rather than measure-theoretical approach to the derivation of the partial differential equations of nonlinear filtering theory. The basis for this approach is the discre

Based on the many approaches available for dealing with large-scale systems (LSS), Decentralized Control and Filtering in Interconnected Dynamical Systems supplies a rigorous framework for studying th

This second edition is an extensive modernization of the bestselling introduction to the subject of adaptive array sensor systems. With the number of applications of adaptive array sensor systems grow

This book is about adaptive mesh generation and moving mesh methods for the numerical solution of time-dependent partial differential equations. It presents a general framework and theory for adaptive

This book provides a comprehensive account of stochastic filtering as a modeling tool in finance and economics. It aims to present this very important tool with a view to making it more popular among

Adaptive management is learning from past management actions to improve future planning. In order to facilitate better adaptive management practices, this book presents lessons learned from case studi

An expert introduction to stage-wise adaptive designs in all areas of statistics Stage-Wise Adaptive Designs presents the theory and methodology of stage-wise adaptive design across various areas of

The purpose of this book is to provide a rigorous mathematical treatment of the non-linear stochastic filtering problem using modern methods. Particular emphasis is placed on the theoretical analysis

This book constitutes the refereed proceedings of the 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH 2008, held in Hannover, Germany in July 2008.The 24 revised

Stochastic Filtering Theory uses probability tools to estimate unobservable stochastic processes that arise in many applied fields including communication, target-tracking, and mathematical finance.As

Grewal (California State U. at Fullerton) and Andrews, retired from the Rockwell Science Center, explain the theoretical and practical aspects of Kalman filtering through practice and real world examp

Seismic inverse Q filtering is a data processing technology for enhancing the resolution of seismic images. It employs a wave propagation reversal procedure that compensates for energy absorption and

This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are adv

The first comprehensive development of Bayesian Bounds for parameter estimation and nonlinear filtering/tracking Bayesian estimation plays a central role in many signal processing problems encountere

Adaptive structures have the ability to adapt, evolve or change their properties or behaviour in response to the environment around them. The analysis and design of adaptive structures requires a high

Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from the extraction of noise signals. Moreover, it is a fundamenta

This third edition of "Principles of Space-Time Adaptive Processing" provides a detailed introduction to the fundamentals of space-time adaptive processing, with emphasis on clutter suppression in ai

The latest research and developments in robust adaptive beamforming Recent work has made great strides toward devising robust adaptive beamformers that vastly improve signal strength against backgrou

This graduate-level text focuses on the stability of adaptive systems, and offers a thorough understanding of the global stability properties essential to designing adaptive systems. Its self-containe

This new text, from the author of the highly-successful Principles of Space-Time Adaptive Processing (IEE, 2002, see below), discusses various applications of space-time adaptive processing, includin