Nonlinear Filtering

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.

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.

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

1.5 Digital Si…