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Nonlinear filtering : concepts and engineering applications / authors, Jitendra R. Raol, Bhekisipho Twala and Girija Gopalratnam.

By: Contributor(s): Material type: TextTextPublisher: Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, [2017]Description: 1 online resource (xxviii, 556 pages)ISBN:
  • 9781315151908
  • 9781351638425
Subject(s): Additional physical formats: Print version: : No titleDDC classification:
  • 519.23 R215
LOC classification:
  • QA274 .R38 2017
Online resources:
Contents:
part Section I Mathematical Models, Kalman Filtering and H-Infinity Filters -- chapter 1 Dynamic System Models and Basic Concepts -- chapter 2 Filtering and Smoothing -- chapter 3 H8 Filtering -- chapter 4 Adaptive Filtering -- part Section II Factorization and Approximation Filters -- chapter 5 Factorization Filtering -- chapter 6 Approximation Filters for Nonlinear Systems -- chapter 7 Generalized Model Error Estimators for Nonlinear Systems -- part Section III Nonlinear Filtering, Estimation and Implementation Approaches -- chapter 8 Nonlinear Estimation and Filtering -- chapter 9 Nonlinear Filtering Based on Characteristic Functions -- chapter 10 Implementation Aspects of Nonlinear Filters -- chapter 11 Nonlinear Parameter Estimation -- chapter 12 Nonlinear Observers -- part Section IV Appendixes Basic Concepts and Supporting Material -- chapter Section IV Appendixes Basic Concepts and Supporting Material Appendix A: System Theoretic Concepts Controllability, Observability, Identifiability and Estimability.
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.
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Includes bibliographical references.

part Section I Mathematical Models, Kalman Filtering and H-Infinity Filters -- chapter 1 Dynamic System Models and Basic Concepts -- chapter 2 Filtering and Smoothing -- chapter 3 H8 Filtering -- chapter 4 Adaptive Filtering -- part Section II Factorization and Approximation Filters -- chapter 5 Factorization Filtering -- chapter 6 Approximation Filters for Nonlinear Systems -- chapter 7 Generalized Model Error Estimators for Nonlinear Systems -- part Section III Nonlinear Filtering, Estimation and Implementation Approaches -- chapter 8 Nonlinear Estimation and Filtering -- chapter 9 Nonlinear Filtering Based on Characteristic Functions -- chapter 10 Implementation Aspects of Nonlinear Filters -- chapter 11 Nonlinear Parameter Estimation -- chapter 12 Nonlinear Observers -- part Section IV Appendixes Basic Concepts and Supporting Material -- chapter Section IV Appendixes Basic Concepts and Supporting Material Appendix A: System Theoretic Concepts Controllability, Observability, Identifiability and Estimability.

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.

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