eBook - ePub
Advances in State and Parameter Estimation
Theory and Practice
- 384 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Advances in State and Parameter Estimation
Theory and Practice
About this book
This book deals with the basics of parameter estimation and state estimation as the fundamental building blocks of mathematical modelling activity in the broader field of control theory. All the methods are validated using MATLABĀ®-based implementations with realistically simulated data for general dynamic systems, as well as for aircraft parameter estimation. This book includes several illustrative examples and chapter-end exercises.
Features:
- Provides comprehensive coverage of all issues related to parameter and state estimation.
- Discusses advanced topics related to Kalman filter, stability analysis, image centroid tracking and neural networks for parameter estimation.
- Explores convergence and stability results for the discussed methods.
- Reviews the estimation of parameters in linear/nonlinear models, and distributed fitting.
- Includes MATLABĀ®-based illustrative examples, and exercises.
This book is aimed at researchers and graduate students in systems and control, signal processing, estimation theory, engineering mathematics, and aerospace engineering.
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Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Advances in State and Parameter Estimation by Jitendra R. Raol,Parimala P.,Reshma V.,Sara M. George in PDF and/or ePUB format. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover
- Half-Title Page
- Title Page
- Copyright Page
- Table of Contents
- Preface
- Acknowledgements
- 1 Introduction
- 2 Least-Squares and Maximum-Likelihood Methods
- 3 Kalman Filtering Methods
- 4 Filtering-cum-Data Fusion with State Delay and Missing Measurements*
- 5 Gaussian-Sum Extended Kalman Filter with Lyapunov Stability Analysis*
- 6 Gaussian-Sum Information Filter with Lyapunov Stability Analysis
- 7 Image Centroid Tracking with Square-Root Kalman Filters
- 8 Image Centroid Tracking with Fuzzy-Logic-Augmented Filters
- 9 Image Centroid Tracking-cum-Fusion using New Factorization Filtering Algorithms
- 10 H-Infinity Fuzzification Filtering and Target Tracking
- 11 H-Infinity-Based Observer
- 12 Deterministic Nonlinear EstimatorsāObservers and Stability Results*
- 13 Hybrid Global H-Infinity Fusion Filter
- 14 Neural Networks for Parameter Estimation with Lyapunov Stability
- 15 Interactive Multiple Modelling for Target Tracking with New Algorithms
- 16 Machine Learning for Estimation
- Mathematical Modelling and System Identification
- Sensors, Signals and Estimation-Error Propagation
- Neural Networks, Fuzzy Logic and Genetic Algorithms for Estimation
- Index