Optimal State Estimation

Kalman, H Infinity, and Nonlinear Approaches

Author: Dan Simon

Publisher: John Wiley & Sons

ISBN: 0470045337

Category: Technology & Engineering

Page: 552

View: 2575

A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.
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Optimal State Estimation

Kalman, H Infinity, and Nonlinear Approaches

Author: Dan Simon

Publisher: Wiley-Interscience

ISBN: 0470045337

Category: Technology & Engineering

Page: 558

View: 4462

A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.
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Optimal Filtering

Author: Brian D. O. Anderson,John B. Moore

Publisher: Courier Corporation

ISBN: 0486136892

Category: Science

Page: 368

View: 2503

Graduate-level text extends studies of signal processing, particularly regarding communication systems and digital filtering theory. Topics include filtering, linear systems, and estimation; discrete-time Kalman filter; time-invariant filters; more. 1979 edition.
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Stochastic Processes and Filtering Theory

Author: Andrew H. Jazwinski

Publisher: Courier Corporation

ISBN: 0486318192

Category: Science

Page: 400

View: 3807

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 advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well. Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probability theory and stochastic processes, the author introduces and defines the problems of filtering, prediction, and smoothing. He presents the mathematical solutions to nonlinear filtering problems, and he specializes the nonlinear theory to linear problems. The final chapters deal with applications, addressing the development of approximate nonlinear filters, and presenting a critical analysis of their performance.
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Estimation with Applications to Tracking and Navigation

Theory Algorithms and Software

Author: Yaakov Bar-Shalom,X. Rong Li,Thiagalingam Kirubarajan

Publisher: John Wiley & Sons

ISBN: 0471465216

Category: Technology & Engineering

Page: 584

View: 6198

Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics. The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimation with a focus on applying these techniques to real systems. Other features include: Problems that apply theoretical material to real-world applications In-depth coverage of the Interacting Multiple Model (IMM) estimator Companion DynaEst(TM) software for MATLAB(TM) implementation of Kalman filters and IMM estimators Design guidelines for tracking filters Suitable for graduate engineering students and engineers working in remote sensors and tracking, Estimation with Applications to Tracking and Navigation provides expert coverage of this important area.
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Kalman Filtering and Neural Networks

Author: Simon Haykin

Publisher: John Wiley & Sons

ISBN: 047146421X

Category: Technology & Engineering

Page: 284

View: 5264

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. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley Makerting Department.
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Applied Optimal Estimation

Author: Arthur Gelb

Publisher: MIT Press

ISBN: 9780262570480

Category: Computers

Page: 374

View: 3711

This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation. Even so, theoretical and mathematical concepts are introduced and developed sufficiently to make the book a self-contained source of instruction for readers without prior knowledge of the basic principles of the field. The work is the product of the technical staff of the The Analytic Sciences Corporation (TASC), an organization whose success has resulted largely from its applications of optimal estimation techniques to a wide variety of real situations involving large-scale systemsArthur Gelb writes in the Foreword that "It is our intent throughout to provide a simple and interesting picture of the central issues underlying modern estimation theory and practice. Heuristic, rather than theoretically elegant, arguments are used extensively, with emphasis on physical insights and key questions of practical importance."Numerous illustrative examples, many based on actual applications, have been interspersed throughout the text to lead the student to a concrete understanding of the theoretical material. The inclusion of problems with "built-in" answers at the end of each of the nine chapters further enhances the self-study potential of the text.After a brief historical prelude, the book introduces the mathematics underlying random process theory and state-space characterization of linear dynamic systems. The theory and practice of optimal estimation is them presented, including filtering, smoothing, and prediction. Both linear and non-linear systems, and continuous- and discrete-time cases, are covered in considerable detail. New results are described concerning the application of covariance analysis to non-linear systems and the connection between observers and optimal estimators. The final chapters treat such practical and often pivotal issues as suboptimal structure, and computer loading considerations.This book is an outgrowth of a course given by TASC at a number of US Government facilities. Virtually all of the members of the TASC technical staff have, at one time and in one way or another, contributed to the material contained in the work
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Stochastic Models, Estimation, and Control

Author: Peter S. Maybeck

Publisher: Academic Press

ISBN: 9780080960036

Category: Mathematics

Page: 291

View: 9618

This volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws.
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Evolutionary Optimization Algorithms

Author: Dan Simon

Publisher: John Wiley & Sons

ISBN: 1118659503

Category: Mathematics

Page: 772

View: 9253

A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear—but theoretically rigorous—understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs—including opposition-based learning, artificial fish swarms, bacterial foraging, and many others— and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.
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Matrix Methods in the Design Analysis of Mechanisms and Multibody Systems

Author: John J. Uicker,Pradip N. Sheth,Bahram Ravani

Publisher: Cambridge University Press

ISBN: 0521761093

Category: Computers

Page: 326

View: 3404

This is an integrated approach to kinematic and dynamic analysis. The matrix techniques presented are general and applicable to two- or three-dimensional systems. The techniques lend themselves to programming and digital computation and can be a usable tool for designers, and are applicable to the design analysis of all multibody mechanical systems.
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Kalman Filtering

Theory and Practice with MATLAB

Author: Mohinder S. Grewal,Angus P. Andrews

Publisher: John Wiley & Sons

ISBN: 111898496X

Category: Technology & Engineering

Page: 640

View: 698

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. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.
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Global Positioning System

Theory and Applications

Author: Bradford W. Parkinson

Publisher: AIAA

ISBN: 9781600864193

Category: Artificial satellites in navigation

Page: 781

View: 3668

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Optimal Control and Estimation

Author: Robert F. Stengel

Publisher: Courier Corporation

ISBN: 0486134814

Category: Mathematics

Page: 672

View: 2198

Graduate-level text provides introduction to optimal control theory for stochastic systems, emphasizing application of basic concepts to real problems.
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Linear Estimation

Author: Thomas Kailath,Ali H. Sayed,Babak Hassibi

Publisher: N.A

ISBN: 9780130224644

Category: Mathematics

Page: 854

View: 2478

This original work offers the most comprehensive and up-to-date treatment of the important subject of optimal linear estimation, which is encountered in many areas of engineering such as communications, control, and signal processing, and also in several other fields, e.g., econometrics and statistics. The book not only highlights the most significant contributions to this field during the 20th century, including the works of Wiener and Kalman, but it does so in an original and novel manner that paves the way for further developments. This book contains a large collection of problems that complement it and are an important part of piece, in addition to numerous sections that offer interesting historical accounts and insights. The book also includes several results that appear in print for the first time. FEATURES/BENEFITS Takes a geometric point of view. Emphasis on the numerically favored array forms of many algorithms. Emphasis on equivalence and duality concepts for the solution of several related problems in adaptive filtering, estimation, and control. These features are generally absent in most prior treatments, ostensibly on the grounds that they are too abstract and complicated. It is the authors' hope that these misconceptions will be dispelled by the presentation herein, and that the fundamental simplicity and power of these ideas will be more widely recognized and exploited. Among other things, these features already yielded new insights and new results for linear and nonlinear problems in areas such as adaptive filtering, quadratic control, and estimation, including the recent "Ha theories."
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Inertial Navigation Systems Analysis

Author: Kenneth Robert Britting

Publisher: Artech House on Demand

ISBN: 9781608070787

Category: Reference

Page: 249

View: 4062

Out-of-print for years, this highly sought-after volume, remains the most popular reference on inertial navigation systems analysis. Finally, this classic book is back in print and readily available only from Artech House. Authored by a pioneer in the field, this authoritative resource focuses on terrestrial navigation, but is also useful for air and sea applications. Packed with valuable, time-saving equations and models, the book helps engineers design optimal navigation systems by comparing the performance of the various types of system mechanizations. Although applications and technology have changed over the years, this book remains the best source for fundamental inertial navigation system knowledge, from notational conventions, reference frames, and geometry of the earth, to unified error analysis, self-alignment techniques, and the development of a system error model. This well-illustrated, timeless reference belongs on the shelf of every practicing engineer working in this area.
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Global Positioning System

Signals, Measurements, and Performance

Author: Pratap Misra,Per Enge

Publisher: N.A

ISBN: 9780970954428

Category: Global Positioning System

Page: 569

View: 5775

Accompanying CD-ROM contains a number of GPS data sets from several sites. A set of homework problems requires the student to write simple MATLAB code to analyze these data.
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Bayesian Signal Processing

Classical, Modern, and Particle Filtering Methods

Author: James V. Candy

Publisher: John Wiley & Sons

ISBN: 1119125480

Category: Technology & Engineering

Page: 640

View: 4353

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® 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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Optimal Estimation of Dynamic Systems

Author: John L. Crassidis,John L. Junkins

Publisher: CRC Press

ISBN: 1135439273

Category: Mathematics

Page: 608

View: 9485

Most newcomers to the field of linear stochastic estimation go through a difficult process in understanding and applying the theory.This book minimizes the process while introducing the fundamentals of optimal estimation. Optimal Estimation of Dynamic Systems explores topics that are important in the field of control where the signals received are used to determine highly sensitive processes such as the flight path of a plane, the orbit of a space vehicle, or the control of a machine. The authors use dynamic models from mechanical and aerospace engineering to provide immediate results of estimation concepts with a minimal reliance on mathematical skills. The book documents the development of the central concepts and methods of optimal estimation theory in a manner accessible to engineering students, applied mathematicians, and practicing engineers. It includes rigorous theoretial derivations and a significant amount of qualitiative discussion and judgements. It also presents prototype algorithms, giving detail and discussion to stimulate development of efficient computer programs and intelligent use of them. This book illustrates the application of optimal estimation methods to problems with varying degrees of analytical and numercial difficulty. It compares various approaches to help develop a feel for the absolute and relative utility of different methods, and provides many applications in the fields of aerospace, mechanical, and electrical engineering.
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Introductory Mining Engineering

Author: Howard L. Hartman,Jan M. Mutmansky

Publisher: John Wiley & Sons

ISBN: 9780471348511

Category: Technology & Engineering

Page: 570

View: 1773

Hartman (mining engineering, U. of Alabama) and Mutmansky (emeritus, mining engineering, Pennsylvania State U. at University Park) have two primary aims in the presentation of the second edition of their mining textbook: to familiarize readers with the minerals industry and to aid in the selection of the optimal methods for the specified conditions. To achieve those goals they present 14 chapters covering mining and its consequences, prospecting and exploration, development and exploitation, unit operations of mining, surface mine development, mechanical extraction methods, aqueous extraction methods, underground mine development, unsupported and supported methods in underground mining, caving methods, and novel methods and technology. Annotation c. Book News, Inc., Portland, OR (booknews.com).
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