عنوان

Grid-based nonlinear estimation and its applications /

(Number (ISBN

1315193213

(Number (ISBN

1351757393

(Number (ISBN

1351757407

(Number (ISBN

1351757415

(Number (ISBN

9781315193212

(Number (ISBN

9781351757393

(Number (ISBN

9781351757409

(Number (ISBN

9781351757416

Erroneous ISBN

1138723096

Erroneous ISBN

9781138723092

Title Proper

Grid-based nonlinear estimation and its applications /

General Material Designation

[Book]

First Statement of Responsibility

Bin Jia (Intelligent Fusion Technology, Inc., Germantown, Maryland, USA), Ming Xin (Department of Mechanical and Aerospace Engineering [University of Missouri]).

Place of Publication, Distribution, etc.

Boca Raton, FL :

Name of Publisher, Distributor, etc.

CRC Press, Taylor & Francis Group,

Date of Publication, Distribution, etc.

2019.

Specific Material Designation and Extent of Item

1 online resource

Text of Note

"A science publishers book."

Text of Note

Includes bibliographical references and index.

Text of Note

Cover; Title Page; Copyright Page; Table of Contents; Preface; 1: Introduction; 1.1 Random Variables and Random Process; 1.2 Gaussian Distribution; 1.3 Bayesian Estimation; References; 2: Linear Estimation of Dynamic Systems; 2.1 Linear Discrete-Time Kalman Filter; 2.2 Information Kalman Filter; 2.3 The Relation Between the Bayesian Estimation and Kalman Filter; 2.4 Linear Continuous-Time Kalman Filter; References; 3: Conventional Nonlinear Filters; 3.1 Extended Kalman Filter; 3.2 Iterated Extended Kalman Filter; 3.3 Point-Mass Filter; 3.4 Particle Filter; 3.5 Combined Particle Filter

Text of Note

3.5.1 Marginalized Particle Filter3.5.2 Gaussian Filter Aided Particle Filter; 3.6 Ensemble Kalman Filter; 3.7 Zakai Filter and Fokker Planck Equation; 3.8 Summary; References; 4: Grid-based Gaussian Nonlinear Estimation; 4.1 General Gaussian Approximation Nonlinear Filter; 4.2 General Gaussian Approximation Nonlinear Smoother; 4.3 Unscented Transformation; 4.4 Gauss-Hermite Quadrature; 4.5 Sparse-Grid Quadrature; 4.6 Anisotropic Sparse-Grid Quadrature and Accuracy Analysis; 4.6.1 Anisotropic Sparse-Grid Quadrature; 4.6.2 Analysis of Accuracy of the Anisotropic Sparse-Grid Quadrature

Text of Note

4.7 Spherical-Radial Cubature4.8 The Relations Among Unscented Transformation, Sparse-Grid Quadrature, and Cubature Rule; 4.8.1 From the Spherical-Radial Cubature Rule to the Unscented Transformation; 4.8.2 The Connection between the Quadrature Rule and the Spherical Rule; 4.8.3 The Relations Between the Sparse-Grid Quadrature Rule and the Spherical-Radial Cubature Rule; 4.9 Positive Weighted Quadrature; 4.10 Adaptive Quadrature; 4.10.1 Global Measure of Nonlinearity for Stochastic Systems; 4.10.2 Local Measure of Nonlinearity for Stochastic Systems; 4.11 Summary; References

Text of Note

5.9 Interacting Multiple Model Filter5.10 Summary; References; 6: Multiple Sensor Estimation; 6.1 Main Fusion Structures; 6.2 Grid-based Information Kalman Filters and Centralized Gaussian Nonlinear Estimation; 6.3 Consensus-based Strategy; 6.3.1 Consensus Algorithm; 6.3.2 Consensus-based Filter; 6.4 Covariance Intersection Strategy; 6.4.1 Covariance Intersection; 6.4.2 Iterative Covariance Intersection; 6.4.3 Distributed Batch Covariance Intersection; 6.4.4 Analysis; 6.5 Diffusion-based Strategy; 6.6 Distributed Particle Filter; 6.7 Multiple Sensor Estimation and Sensor Allocation

Text of Note

5: Nonlinear Estimation: Extensions5.1 Grid-based Continuous-Discrete Gaussian Approximation Filter; 5.2 Augmented Grid-based Gaussian Approximation Filter; 5.3 Square-root Grid-based Gaussian Approximation Filter; 5.4 Constrained Grid-based Gaussian Approximation Filter; 5.4.1 Interval-constrained Unscented Transformation; 5.4.2 Estimation Projection and Constrained Update; 5.5 Robust Grid-based Gaussian Approximation Filter; 5.5.1 Huber-based Filter; 5.5.2 H∞ Filter; 5.6 Gaussian Mixture Filter; 5.7 Simplified Grid-based Gaussian Mixture Filter; 5.8 Adaptive Gaussian Mixture Filter

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Text of Note

Grid-based Nonlinear Estimation and its Applications presents new Bayesian nonlinear estimation techniques developed in the last two decades. Grid-based estimation techniques are based on efficient and precise numerical integration rules to improve performance of the traditional Kalman filtering based estimation for nonlinear and uncertainty dynamic systems. The unscented Kalman filter, Gauss-Hermite quadrature filter, cubature Kalman filter, sparse-grid quadrature filter, and many other numerical grid-based filtering techniques have been introduced and compared in this book. Theoretical analysis and numerical simulations are provided to show the relationships and distinct features of different estimation techniques. To assist the exposition of the filtering concept, preliminary mathematical review is provided. In addition, rather than merely considering the single sensor estimation, multiple sensor estimation, including the centralized and decentralized estimation, is included. Different decentralized estimation strategies, including consensus, diffusion, and covariance intersection, are investigated. Diverse engineering applications, such as uncertainty propagation, target tracking, guidance, navigation, and control, are presented to illustrate the performance of different grid-based estimation techniques.

Source for Acquisition/Subscription Address

Taylor & Francis

Stock Number

9781315193212

Title

Grid-based nonlinear estimation and its applications.

International Standard Book Number

9781138723092

Estimation theory.

Nonlinear theories.

Estimation theory.

MATHEMATICS-- Applied.

Nonlinear theories.

SCIENCE-- Life Sciences-- General.

TECHNOLOGY-- Electricity.

MAT-- 003000

SCI-- 086000

TEC-- 007000

TQ

Number

Edition

Class number

Book number

Jia, Bin,1982-

Xin, Ming,1972-

Date of Transaction

20200822164159.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y