‏849.00 ₪

Grid-based Nonlinear Estimation and Its Applications

‏849.00 ₪

ISBN13

9781138723092

יצא לאור ב

London

עמודים

260

פורמט

Hardback

תאריך יציאה לאור

23 במאי 2019

לדלג לסוף של גלריית תמונות

לדלג להתחלה של גלריית תמונות

פרטים

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.

מידע נוסף

מידע נוסף
עמודים	260
פורמט	Hardback
ISBN10	1138723096
יצא לאור ב	London
תאריך יציאה לאור	23 במאי 2019
תוכן עניינים	Contents Introduction Random variables and random process Gaussian distribution Bayesian estimation Reference Linear Estimation of Dynamic Systems Linear Discrete-Time Kalman Filter Information Kalman Filter The relation between the Bayesian Estimation and Kalman Filter Linear Continuous-Time Kalman Filter Reference Conventional Nonlinear Filters Extended Kalman Filter Iterated Extended Kalman Filter Point-mass Filter Particle Filter Combined Particle Filter Ensemble Kalman Filter Zakai Filter and Fokkle Planck Equation Summary Reference Grid-Based Gaussian Nonlinear Estimation General Gaussian Approximation Nonlinear Filter General Gaussian Approximation Nonlinear Smoother Unscented Transformation Gauss-Hermite Quadrature Sparse-Grid Quadrature Anisotropic Sparse-grid Quadrature and Accuracy Analysis Spherical-Radial Cubature The relation among Unscented Transformation, Sparse-Grid Quadrature, and Cubature Rule Positive Weighted Quadrature Adaptive Quadrature Summary Reference Nonlinear Estimation: Extensions Grid-based Continuous-Discrete Gaussian Approximation Kalman Filter Augmented Grid-based Gaussian Approximation Filter Square-root Grid-based Gaussian Approximation Filter Constrained Grid-based Gaussian Approximation Filter Robust Grid-based Gaussian Approximation Filter Gaussian Mixture Filter Simplified Grid-based Gaussian Mixture Filter Adaptive Gaussian Mixture Filter Interacting Multiple Model Filter Summary Reference Multiple Sensor Estimation Main Fusion Structures Grid-based Information Kalman Filters and Centralized Gaussian Nonlinear Estimation Consensus-based Strategy Covariance Intersection Strategy Diffusion-based Strategy Distributed Particle Filter Multiple Sensor Estimation and Sensor Allocation Summary Reference Application: Uncertainty Propagation Gaussian Quadrature -based Uncertainty Propagation Multi-element Grid-based Uncertainty Propagation Uncertainty Propagator Gaussian Mixture based Uncertainty Propagation Stochastic Expansion based Uncertainty Propagation Graphic Process Unit aided Uncertainty Propagation MapReduce aided Uncertainty Propagation Summary Reference Application: Tracking and Navigation Single Target Tracking Multiple Target Tracking Spacecraft Relative Attitude Estimation Summary Reference