Introduction to Random Signals, Estimation Theory, and Kalman Filtering / by M. Sami Fadali.

Format:

Book

Author/Creator:

Fadali, M. Sami, author.

Language:

English

Subjects (All):

Automatic control.

Robotics.

Automation.

Aerospace engineering.

Astronautics.

Telecommunication.

Control, Robotics, Automation.

Aerospace Technology and Astronautics.

Communications Engineering, Networks.

Local Subjects:

Control, Robotics, Automation.

Aerospace Technology and Astronautics.

Communications Engineering, Networks.

Physical Description:

1 online resource (489 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

This book provides first-year graduate engineering students and practicing engineers with a solid introduction to random signals and estimation. It includes a statistical background that is often omitted in other textbooks but is essential for a clear understanding of estimators and their properties. The book emphasizes applicability rather than mathematical theory. It includes many examples and exercises to demonstrate and learn the theory that makes extensive use of MATLAB and its toolboxes. Although there are several excellent books on random signals and Kalman filtering, this book fulfills the need for a book that is suitable for a single-semester course that covers both random signals and Kalman filters and is used for a two-semester course for students that need remedial background. For students interested in more advanced studies in the area, the book provides a bridge between typical undergraduate engineering education and more advanced graduate-level courses.

Contents:

Review of Probability Theory

Random Variables

Random Signals (autocorrelation, power spectral density)

Response of Linear Systems to Random Inputs (continuous, discrete)

Estimation and Estimator Properties (small sample and large sample properties of estimators, CRLB)

Least Square Estimation Likelihood (likelihood function, detection)

Maximum Likelihood Estimation

Minimum Mean-Square Error Estimation (Kalman Filter, information filter, filter stability)

Generalizing the Basic Kalman Filter (colored noise, correlated noise, reduced-order estimator, Schmidt Kalman filter sequential computation)

Prediction and Smoothing

Nonlinear Filtering (Extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, particle filter)

The Expectation Maximization Algorithm

Markov Models.

Notes:

Includes bibliographical references and index.

ISBN:

981-9980-63-1