Stochastic models, estimation and control. Volume 1 / Peter S. Maybeck.

Format:

Book

Author/Creator:

Maybeck, Peter S.

Series:

Mathematics in science and engineering ; v. 141.

Mathematics in science and engineering ; v. 141

Language:

English

Subjects (All):

System analysis.

Control theory.

Estimation theory.

Physical Description:

1 online resource (445 p.)

Place of Publication:

New York : Academic Press, 1979.

Language Note:

English

Summary:

Stochastic Models: Estimation and Control: v. 1

Contents:

Front Cover; Stochastic models, estimation, and control; Copyright Page; Contents; Preface; Contents of Volume 2; 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; General References; Appendix and Problems; References; Chapter 2. Deterministic system models; 2.1 Introduction; 2.2 Continuous-Time Dynamic Models; 2.3 Solutions to State Differential Equations; 2.4 Discrete-Time Measurements

2.5 Controllability and Observability2.6 Summary; References; Problems; Chapter 3. Probability theory and static models; 3.1 Introduction; 3.2 Probability and Random Variables; 3.3 Probability Distributions and Densities; 3.4 Conditional Probability and Densities; 3.5 Functions of Random Variables; 3.6 Expectation and Moments of Random Variables; 3.7 Conditional Expectations; 3.8 Characteristic Functions; 3.9 Gaussian Random Vectors; 3.10 Linear Operations on Gaussian Random Variables; 3.11 Estimation with Static Linear Gaussian System Models; 3.12 Summary; References; Problems

Chapter 4. Stochastic processes and linear dynamic system models4.1 Introduction; 4.2 Stochastic Processes; 4.3 Stationary Stochastic Processes and Power Spectral Density; 4.4 System Modeling: Objectives and Directions; 4.5 Foundations: White Gaussian Noise and Brownian Motion; 4.6 Stochastic Integrals; 4.7 Stochastic Differentials; 4.8 Linear Stochastic Differential Equations; 4.9 Linear Stochastic Difference Equations; 4.10 The Overall System Model; 4.11 Shaping Filters and State Augmentation; 4.12 Power Spectrum Concepts and Shaping Filters; 4.13 Generating Practical System Models

4.14 SummaryReferences; Problems; Chapter 5. Optimal filtering with linear system models; 5.1 Introduction; 5.2 Problem Formulation; 5.3 The Discrete-Time (Sampled Data) Optimal Estimator: The Kalman Filter; 5.4 Statistics of Processes within the Filter Structure; 5.5 Other Criteria of Optimality; 5.6 Covariance Measurement Update Computations; 5.7 Inverse Covariance Form; 5.8 Stability; 5.9 Correlation of Dynamic Driving Noise and Measurement Noise; 5.10 Time-Correlated Measurement Noise: Perfect Measurements; 5.11 Continuous-Time Filter; 5.12 Wiener Filtering and Frequency Domain Techniques

5.13 SummaryReferences; Problems; Chapter 6. Design and performance analysis of Kalman filters; 6.1 Introduction; 6.2 The Requisite of Engineering Judgment; 6.3 Application of Kalman Filtering to Inertial Navigation Systems; 6.4 INS Aided by Position Data: A Simple Example; 6.5 Doppler-Aided INS; 6.6 INS Calibration and Alignment Using Direct Kalman Filter; 6.7 Generating Alternative Designs; 6.8 Performance (Sensitivity) Analysis; 6.9 Systematic Design Procedure; 6.10 INS Aided by Navigation Satellites; 6.11 Practical Aspects of Implementation; 6.12 Summary; References; Problems

Chapter 7. Square root filtering

Notes:

Description based upon print version of record.

Includes bibliographies and index.

ISBN:

1-282-29028-2

9786612290282

0-08-095650-5

OCLC:

466443953