| Description |
1 online resource (556 pages) : illustrations |
| Contents |
Cover; Half Title; Title; Copyright; Dedication; Contents; Preface; Acknowledgements; Authors; Introduction; Section I Mathematical Models, Kalman Filtering and H-Infinity Filters; Chapter 1. Dynamic System Models and Basic Concepts; 1.1 Dynamic Systems: The Need for Modelling, Parameter Estimation and Filtering; 1.2 Mathematical Modelling of Systems; 1.2.1 Time and Frequency Domain Aspects; 1.2.2 Differential Equations; 1.2.3 Difference Equations; 1.2.4 State Space Models; 1.2.4.1 Physical Representation; 1.2.4.2 Controllable Canonical Form; 1.2.4.3 Observable Canonical Form |
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1.2.4.4 Diagonal Form1.2.4.5 General State Space Models; 1.2.5 Polynomial Models; 1.2.6 Time Series Models; 1.2.6.1 Autoregressive Model; 1.2.6.2 Least Squares Model; 1.2.7 Transfer Function Models; 1.3 Nonlinear Dynamic Systems; 1.3.1 Nonlinearities in a System; 1.3.2 Mathematical Models of Nonlinear Systems; 1.3.2.1 Nonlinear Differential and Difference Equations; 1.3.2.2 Volterra Series; 1.3.2.3 Hammerstein Model; 1.3.2.4 Nonlinear State Space Models; 1.3.2.5 Nonlinear Time Series Models; 1.4 Signal and System Norms; 1.4.1 Signal Norms; 1.4.2 System Norms; 1.4.2.1 H2 Norm; 1.4.2.2 H∞ Norm |
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1.5 Digital Signal Processing, Parameter Estimation and Filtering1.5.1 Signal Processing; 1.5.2 Parameter Estimation: Recursive Approach; 1.5.3 Filtering Concepts; 1.5.4 Simple Recursive Filtering; Appendix 1A: Mean Square Estimation; Appendix 1B: Nonlinear Models Based on Artificial Neural Networks and Fuzzy Logic; Appendix 1C: Illustrative Examples; Chapter 2. Filtering and Smoothing; 2.1 Wiener Filtering; 2.2 Least Squares Parameter Estimation; 2.3 Recursive Least Squares Filter; 2.4 State Space Models and Kalman Filtering; 2.4.1 Discrete Time Filter |
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2.4.1.1 State and Covariance Matrix Propagation2.4.1.2 Measurement Update; 2.4.1.3 Kalman Gain; 2.4.2 Continuous Time Kalman Filter; 2.4.3 Interpretation of Kalman Filter; 2.4.3.1 Continuous Time Filter; 2.4.3.2 Discrete Time Filter; 2.4.4 Filters for Correlated/Coloured Process and Measurement Noises; 2.4.4.1 Kalman Filter for the Correlated Process and Measurement Noises; 2.4.4.2 Handling of Coloured Process Noise and Coloured Measurement Noise in Kalman Filters; 2.4.5 Time-Varying Linear Kalman Filters; 2.4.6 Steady State Filtering; 2.4.7 Kalman Filter Implementation Aspects |
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2.4.8 Parallelization of Kalman Filters2.4.8.1 Measurement Update Parallelization; 2.4.8.2 Time Propagation Parallelization; 2.5 Filter Error Methods; 2.5.1 Output Error Method; 2.5.2 Process Noise Algorithms for Linear Systems; 2.5.2.1 Natural Formulation; 2.5.2.2 Innovations Formulation; 2.5.2.3 Mixed Formulation; 2.5.3 Process Noise Algorithms for Nonlinear Systems; 2.5.3.1 Steady-State Filter; 2.5.3.2 Time-Varying Filter; 2.6 Information Filtering; 2.6.1 Fisher's Information Concept; 2.6.2 Linear Information Filter; 2.7 Smoothers |
| Summary |
Nonlinear Filtering covers linear and nonlinear filtering in a comprehensive manner, with appropriate theoretic and practical development. Aspects of modeling, estimation, recursive filtering, linear filtering, and nonlinear filtering are presented with appropriate and sufficient mathematics. A modeling-control-system approach is used when applicable, and detailed practical applications are presented to elucidate the analysis and filtering concepts. MATLAB routines are included, and examples from a wide range of engineering applications - including aerospace, automated manufacturing, robotics, and advanced control systems - are referenced throughout the text.--Provided by publisher |
| Notes |
Print version record |
| Subject |
Stochastic processes.
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Filters (Mathematics)
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Nonlinear theory
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Engineering mathematics.
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Stochastic Processes
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MATHEMATICS -- Applied.
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MATHEMATICS -- Probability & Statistics -- General.
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Engineering mathematics
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Filters (Mathematics)
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Stochastic processes
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| Form |
Electronic book
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| ISBN |
1498745180 |
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9781498745185 |
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9781351647953 |
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1351647954 |
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9781315151908 |
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1315151901 |
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