東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

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 filtering/ by M. Sami Fadali.
Author:	Fadali, M. Sami.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xxi, 480 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Stochastic processes. -
Online resource:	https://doi.org/10.1007/978-981-99-8063-5
ISBN:	9789819980635

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

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

520663
Stochastic processes.

LC Class. No.: QA274

Dewey Class. No.: 519.23

Introduction to random signals, estimation theory, and Kalman filtering
LDR:02810nmm a22003255a 4500 001 2370424
003 DE-He213
005 20240402095902.0
006 m d
007 cr nn 008maaau
008 241127s2024 si s 0 eng d
020 $a 9789819980635 $q (electronic bk.)
020 $a 9789819980628 $q (paper)
024 7 $a 10.1007/978-981-99-8063-5 $2 doi
035 $a 978-981-99-8063-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA274
072 7 $a TJFM $2 bicssc
072 7 $a TEC007000 $2 bisacsh
072 7 $a TJFM $2 thema
082 0 4 $a 519.23 $2 23
090 $a QA274 $b .F144 2024
100 1 $a Fadali, M. Sami. $3 931638
245 1 0 $a Introduction to random signals, estimation theory, and Kalman filtering $h [electronic resource] / $c by M. Sami Fadali.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xxi, 480 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Stochastic processes. $3 520663
650 0 $a Estimation theory. $3 565962
650 0 $a Kalman filtering. $3 544465
650 1 4 $a Control, Robotics, Automation. $3 3592500
650 2 4 $a Aerospace Technology and Astronautics. $3 928116
650 2 4 $a Communications Engineering, Networks. $3 891094
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-8063-5
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)