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Optimal estimation and information fusion = theory and algorithms /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimal estimation and information fusion/ by Ming Lei.
其他題名:	theory and algorithms /
作者:	Lei, Ming.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xx, 488 p. :ill., digital ;24 cm.
內容註:	Overview of Stochastic Systems and Basic Concepts of Estimation -- Linear Estimation of Static Systems -- Linear Dynamic Systems with Random Inputs -- State Estimation for Discrete Time Linear Systems -- Extension of State Estimation for Discrete Time Linear Systems -- State Estimation for Discrete Time Nonlinear Systems -- Extended Kalman Filtering Unscented Transform Kalman Filtering -- Particle Filtering -- Theory and Methods of Data Association -- Maneuvering Target Tracking Theory and Methods -- Information Fusion Concepts, Models, and Structures -- Information Fusion Methods -- Application Examples: Algorithms, Simulation, and Analysis.
Contained By:	Springer Nature eBook
標題:	Mathematical optimization. -
電子資源:	https://doi.org/10.1007/978-981-96-3173-5
ISBN:	9789819631735

Optimal estimation and information fusion = theory and algorithms /
Lei, Ming.

Optimal estimation and information fusiontheory and algorithms /[electronic resource] :by Ming Lei. - Singapore :Springer Nature Singapore :2025. - xx, 488 p. :ill., digital ;24 cm.

Overview of Stochastic Systems and Basic Concepts of Estimation -- Linear Estimation of Static Systems -- Linear Dynamic Systems with Random Inputs -- State Estimation for Discrete Time Linear Systems -- Extension of State Estimation for Discrete Time Linear Systems -- State Estimation for Discrete Time Nonlinear Systems -- Extended Kalman Filtering Unscented Transform Kalman Filtering -- Particle Filtering -- Theory and Methods of Data Association -- Maneuvering Target Tracking Theory and Methods -- Information Fusion Concepts, Models, and Structures -- Information Fusion Methods -- Application Examples: Algorithms, Simulation, and Analysis.

This book mainly focuses on the theme of optimizing estimation and sensor information fusion processing for stochastic dynamic systems. It summarizes the basic theories and methods of optimizing estimation and information fusion direction, including stochastic system models, optimal estimation methods, linear state estimation, nonlinear state estimation, information fusion models, structures, data processing methods, data association based on multi-source data estimation, and other aspects. On the basis of years of teaching practice, the author optimizes the content layout, focuses on the basic theoretical methods of the subject, emphasizes the systematic nature of the theory and the rigor of expression, selectively cuts out some outdated content, and introduces some important and widely accepted new developments in the subject. On the other hand, this book also serves as a reference material for technical developers in this field.

ISBN: 9789819631735

Standard No.: 10.1007/978-981-96-3173-5doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Optimal estimation and information fusion = theory and algorithms /
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505 0 $a Overview of Stochastic Systems and Basic Concepts of Estimation -- Linear Estimation of Static Systems -- Linear Dynamic Systems with Random Inputs -- State Estimation for Discrete Time Linear Systems -- Extension of State Estimation for Discrete Time Linear Systems -- State Estimation for Discrete Time Nonlinear Systems -- Extended Kalman Filtering Unscented Transform Kalman Filtering -- Particle Filtering -- Theory and Methods of Data Association -- Maneuvering Target Tracking Theory and Methods -- Information Fusion Concepts, Models, and Structures -- Information Fusion Methods -- Application Examples: Algorithms, Simulation, and Analysis.
520 $a This book mainly focuses on the theme of optimizing estimation and sensor information fusion processing for stochastic dynamic systems. It summarizes the basic theories and methods of optimizing estimation and information fusion direction, including stochastic system models, optimal estimation methods, linear state estimation, nonlinear state estimation, information fusion models, structures, data processing methods, data association based on multi-source data estimation, and other aspects. On the basis of years of teaching practice, the author optimizes the content layout, focuses on the basic theoretical methods of the subject, emphasizes the systematic nature of the theory and the rigor of expression, selectively cuts out some outdated content, and introduces some important and widely accepted new developments in the subject. On the other hand, this book also serves as a reference material for technical developers in this field.
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