This book deals with 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 MATLAB (R) 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 estimation of parameters in linear/nonlinear models, and distributed fitting. Includes MATLAB (R) 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.