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Introduction to random signals, estimation theory, and Kalman filtering

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to random signals, estimation theory, and Kalman filteringby M. Sami Fadali.
Author:	Fadali, M. Sami.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	xxi, 480 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Stochastic processes.
Online resource:	https://doi.org/10.1007/978-981-99-8063-5
ISBN:	9789819980635$q(electronic bk.)

Introduction to random signals, estimation theory, and Kalman filtering
Fadali, M. Sami.

Introduction to random signals, estimation theory, and Kalman filtering[electronic resource] /by M. Sami Fadali. - Singapore :Springer Nature Singapore :2024. - xxi, 480 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789819980635$q(electronic bk.)

Standard No.: 10.1007/978-981-99-8063-5doiSubjects--Topical Terms:

181874
Stochastic processes.

LC Class. No.: QA274

Dewey Class. No.: 519.23

Introduction to random signals, estimation theory, and Kalman filtering
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505 0 $a 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.
520 $a 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.
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