实例介绍
【实例简介】
一部较好地关于，动态模型估计的书籍。希望能够帮助到需要的朋友。
Stochastic models, estimation and control VOLUME 1 PETER S MAYBECK DEPARTMENT OF ELECTRICAL ENGINEERING AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AIR FORCE BASE OHIO ACADEMIC PRESS New York San Francisco London 1979 A Subsidiary of Harcourt Brace Jovanovich, Publishers CoPYRIGHT 1979, BY ACADEMIC PRESS, INC. ALL RIGHTS RESERVED NO PART OF THIS PUBLICATION MAY BE REPRODUCED OR TRANSMITTED IN ANY FORM OR BY ANY MEANS. ELECTRONIC OR MECHANICAL, INCLUDING PHOTOCOPY, RECORDING, OR ANY INFORMATION STORAGE AND RETRIEVAL SYSTEM, WTTHOUT PERMISSION IN WRITING FROM THE PUBLISHER ACADEMIC PRESS. INC 111 Fifth Avenue, New York. New York 10003 United Kingdom Edition published b ACADEMIC PRESS, INC(LONDON)LTD 24/28 Oval Road, London NW1 7DX Library of Congress Cataloging in Publication Data May beck, Peter S Stochastic models estimation and control (Mathematics in science and engineering v. Includes bibliographies. 1. System analysis. 2. Control theory. 3. Estimation theory I. Title. II. Series. QA402M37 519.2 78-8836 ISBN0-12-480701-1(v.1 PRINTED IN THE UNITED STATES OF AMERICA 79808182987654321 To beverly This page intentionally left blank Contents Pref Contents of volume 2 XV Notation Chapter 1 Introduction 1.1 Why Stochastic Models, Estimation, and Control? 1.2 Overview of the Text 1.3 The Kalman Filter: An Introduction to Concepts 1. 4 Basic Assumptions 1.5 A Simple Example 1.6 A Preview 15 General References 15 Appendix and problems 16 References Chapter 2 Deterministic system models 2.2 Continuous-Time Dynamic Models 2. 3 Solutions to State Differential Equations 37 2. 4 Discrete-Time Measurements 42 2.5 Controllability and Observability 43 2.6 Summary Refere Problems Chapter 3 Probability theory and static models 3.1 Introduction 2 Probability and Random variables 3. 3 Probability Distributions and densities 70 3.4 Conditional Probability and Densities 76 CONTENTS 3.5 Functions of random variables 3.6 Expectation and moments of random variables 3.7 Conditional Expectations 38C 3. 9 Gaussian Random vectors 101 3.10 Linear Operations on Gaussian Random Variables 11 3.11 Estimation with Static Linear Gaussian System models 【I4 122 References 122 Problems 123 Chapter 4 Stochastic processes and linear dynamic system models 4.1 Introduction 4.2 Stochastic Processes 133 4.3 Stationary Stochastic Processes and Power Spectral Density 4.4 System Modeling: Objecti 4.5 Foundations: White Gaussian noise and brownian motion 4.6 Stochastic Integrals 156 4.7 Stochastic Differentials 162 4.8 Linear Stochastic Differential Equations 163 4. 9 Linear Stochastic Difference Equations 4. 10 The Overall System Model 174 4. 11 Shaping filters and State augmentation 180 4.12 Power Spectrum Concepts and Shaping Filters 4. 13 Generating Practical System Models y Refer 195 Problems Chapter 5 Optimal filtering with linear system models 5.1 Introduction 5.2 Problem Formulation 203 5.3 The Discrete- Time(Sampled Data) Optimal Estimator The Kalman Filter 206 5.4 Statistics of Processes within the Filter Structure 226 5.5 Other Criteria of optimality 5.6 Covariance Measurement Update Computations 236 5.7 Inverse Covariance Form 238 5.8 Stability 242 5.9 Correlation of Dynamic Driving Noise and measurement Noise 246 5. 10 Time-Correlated Measurement Noise: Perfect Measurements 248 5.11 Continuous-Time Filter 5.12 Wiener Filtering and Frequency Domain Techniques 267 5.13S 275 References 276 Problem CONTENTS Chapter 6 Design and performance analysis of Kalman filters 6.1 Introduction 6.2 The Requisite of Engineering Judgmet 6.3 Application of Kalman Filtering to Inertial Navigation 6.4 INS Aided by Position Data: A Simple Example 6.5 Doppler-Aided INS 305 6.6 INS Calibration and Alignment Using Direct Kalman Filter 317 6.7 Generating Alternative designs 322 6.8 Performance(Sensitivity) Analysis 325 6.9 Systematic Design Procedure 341 6.10 INS Aided by Navigation Satellites 342 6. 11 Practical Aspects of Implementation 351 6.12 Summary 358 References 359 Problems Chapter 7 Square root filtering 7.1 Introduction 368 7.2 Matrix Square roots 370 7.3 Covariance Square Root Filter for Qd=0 373 7.4 Vector-Valued Measurements 374 7.5 Covariance Square Root Filter for Qdt0 377 7.6 Inverse Covariance square root filter 388 7.7 U-D Covariance factorization filter 392 7.8 Filter Performance and requirements 7.9 Summary 405 References 405 Problems 406 Inde 41 This page intentionally left blank 【实例截图】
【核心代码】
一部较好地关于，动态模型估计的书籍。希望能够帮助到需要的朋友。
Stochastic models, estimation and control VOLUME 1 PETER S MAYBECK DEPARTMENT OF ELECTRICAL ENGINEERING AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AIR FORCE BASE OHIO ACADEMIC PRESS New York San Francisco London 1979 A Subsidiary of Harcourt Brace Jovanovich, Publishers CoPYRIGHT 1979, BY ACADEMIC PRESS, INC. ALL RIGHTS RESERVED NO PART OF THIS PUBLICATION MAY BE REPRODUCED OR TRANSMITTED IN ANY FORM OR BY ANY MEANS. ELECTRONIC OR MECHANICAL, INCLUDING PHOTOCOPY, RECORDING, OR ANY INFORMATION STORAGE AND RETRIEVAL SYSTEM, WTTHOUT PERMISSION IN WRITING FROM THE PUBLISHER ACADEMIC PRESS. INC 111 Fifth Avenue, New York. New York 10003 United Kingdom Edition published b ACADEMIC PRESS, INC(LONDON)LTD 24/28 Oval Road, London NW1 7DX Library of Congress Cataloging in Publication Data May beck, Peter S Stochastic models estimation and control (Mathematics in science and engineering v. Includes bibliographies. 1. System analysis. 2. Control theory. 3. Estimation theory I. Title. II. Series. QA402M37 519.2 78-8836 ISBN0-12-480701-1(v.1 PRINTED IN THE UNITED STATES OF AMERICA 79808182987654321 To beverly This page intentionally left blank Contents Pref Contents of volume 2 XV Notation Chapter 1 Introduction 1.1 Why Stochastic Models, Estimation, and Control? 1.2 Overview of the Text 1.3 The Kalman Filter: An Introduction to Concepts 1. 4 Basic Assumptions 1.5 A Simple Example 1.6 A Preview 15 General References 15 Appendix and problems 16 References Chapter 2 Deterministic system models 2.2 Continuous-Time Dynamic Models 2. 3 Solutions to State Differential Equations 37 2. 4 Discrete-Time Measurements 42 2.5 Controllability and Observability 43 2.6 Summary Refere Problems Chapter 3 Probability theory and static models 3.1 Introduction 2 Probability and Random variables 3. 3 Probability Distributions and densities 70 3.4 Conditional Probability and Densities 76 CONTENTS 3.5 Functions of random variables 3.6 Expectation and moments of random variables 3.7 Conditional Expectations 38C 3. 9 Gaussian Random vectors 101 3.10 Linear Operations on Gaussian Random Variables 11 3.11 Estimation with Static Linear Gaussian System models 【I4 122 References 122 Problems 123 Chapter 4 Stochastic processes and linear dynamic system models 4.1 Introduction 4.2 Stochastic Processes 133 4.3 Stationary Stochastic Processes and Power Spectral Density 4.4 System Modeling: Objecti 4.5 Foundations: White Gaussian noise and brownian motion 4.6 Stochastic Integrals 156 4.7 Stochastic Differentials 162 4.8 Linear Stochastic Differential Equations 163 4. 9 Linear Stochastic Difference Equations 4. 10 The Overall System Model 174 4. 11 Shaping filters and State augmentation 180 4.12 Power Spectrum Concepts and Shaping Filters 4. 13 Generating Practical System Models y Refer 195 Problems Chapter 5 Optimal filtering with linear system models 5.1 Introduction 5.2 Problem Formulation 203 5.3 The Discrete- Time(Sampled Data) Optimal Estimator The Kalman Filter 206 5.4 Statistics of Processes within the Filter Structure 226 5.5 Other Criteria of optimality 5.6 Covariance Measurement Update Computations 236 5.7 Inverse Covariance Form 238 5.8 Stability 242 5.9 Correlation of Dynamic Driving Noise and measurement Noise 246 5. 10 Time-Correlated Measurement Noise: Perfect Measurements 248 5.11 Continuous-Time Filter 5.12 Wiener Filtering and Frequency Domain Techniques 267 5.13S 275 References 276 Problem CONTENTS Chapter 6 Design and performance analysis of Kalman filters 6.1 Introduction 6.2 The Requisite of Engineering Judgmet 6.3 Application of Kalman Filtering to Inertial Navigation 6.4 INS Aided by Position Data: A Simple Example 6.5 Doppler-Aided INS 305 6.6 INS Calibration and Alignment Using Direct Kalman Filter 317 6.7 Generating Alternative designs 322 6.8 Performance(Sensitivity) Analysis 325 6.9 Systematic Design Procedure 341 6.10 INS Aided by Navigation Satellites 342 6. 11 Practical Aspects of Implementation 351 6.12 Summary 358 References 359 Problems Chapter 7 Square root filtering 7.1 Introduction 368 7.2 Matrix Square roots 370 7.3 Covariance Square Root Filter for Qd=0 373 7.4 Vector-Valued Measurements 374 7.5 Covariance Square Root Filter for Qdt0 377 7.6 Inverse Covariance square root filter 388 7.7 U-D Covariance factorization filter 392 7.8 Filter Performance and requirements 7.9 Summary 405 References 405 Problems 406 Inde 41 This page intentionally left blank 【实例截图】
【核心代码】
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