Two Cases Of Approximation For Kalman Filtering

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Two Cases Of Approximation For Kalman Filtering

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Author : Michael Antonio Rodriguez
language : en
Publisher:
Release Date : 1971

Two Cases Of Approximation For Kalman Filtering written by Michael Antonio Rodriguez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1971 with categories.

Approximate Kalman Filtering

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Author : Guanrong Chen
language : en
Publisher: World Scientific
Release Date : 1993-08-30

Approximate Kalman Filtering written by Guanrong Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-08-30 with Technology & Engineering categories.

Kalman filtering algorithm gives optimal (linear, unbiased and minimum error-variance) estimates of the unknown state vectors of a linear dynamic-observation system, under the regular conditions such as perfect data information; complete noise statistics; exact linear modeling; ideal well-conditioned matrices in computation and strictly centralized filtering.In practice, however, one or more of the aforementioned conditions may not be satisfied, so that the standard Kalman filtering algorithm cannot be directly used, and hence “approximate Kalman filtering” becomes necessary. In the last decade, a great deal of attention has been focused on modifying and/or extending the standard Kalman filtering technique to handle such irregular cases. It has been realized that approximate Kalman filtering is even more important and useful in applications.This book is a collection of several tutorial and survey articles summarizing recent contributions to the field, along the line of approximate Kalman filtering with emphasis on both its theoretical and practical aspects.

Approximate Analytical Evaluation Of Extended Kalman Filters

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Author : Hasan Öner Tasdelen
language : en
Publisher:
Release Date : 1975

Approximate Analytical Evaluation Of Extended Kalman Filters written by Hasan Öner Tasdelen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1975 with categories.

Analytical equations derived for evaluating linear estimators are applied to extended-Kalman filters for approximate performance evaluation. Two cases were consdidred, a single known target trajectory and multiple target trajectories with given probabilities of occurrence. For the multiple-trajectory case, equations are derived for the mean and covariance of estimation error in terms of the conditional expectations. Two examples are presented to compare the use of the analytical equations with Monte-Carlo simulation.

Bayesian Multiple Target Tracking Second Edition

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Author : Lawrence D. Stone
language : en
Publisher: Artech House
Release Date : 2013-12-01

Bayesian Multiple Target Tracking Second Edition written by Lawrence D. Stone and has been published by Artech House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-01 with Technology & Engineering categories.

This second edition has undergone substantial revision from the 1999 first edition, recognizing that a lot has changed in the multiple target tracking field. One of the most dramatic changes is in the widespread use of particle filters to implement nonlinear, non-Gaussian Bayesian trackers. This book views multiple target tracking as a Bayesian inference problem. Within this framework it develops the theory of single target tracking, multiple target tracking, and likelihood ratio detection and tracking. In addition to providing a detailed description of a basic particle filter that implements the Bayesian single target recursion, this resource provides numerous examples that involve the use of particle filters. With these examples illustrating the developed concepts, algorithms, and approaches -- the book helps radar engineers develop tracking solutions when observations are non-linear functions of target state, when the target state distributions or measurement error distributions are not Gaussian, in low data rate and low signal to noise ratio situations, and when notions of contact and association are merged or unresolved among more than one target.

A Suboptimal Approximation To The Kalman Filter

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Author : Leon Bess
language : en
Publisher:
Release Date : 1969

A Suboptimal Approximation To The Kalman Filter written by Leon Bess and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1969 with Estimation theory categories.

Kalman Filtering

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Author : Charles K. Chui
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29

Kalman Filtering written by Charles K. Chui and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Science categories.

In addition to making a number of minor corrections and updat ing the references, we have expanded the section on "real-time system identification" in Chapter 10 of the first edition into two sections and combined it with Chapter 8. In its place, a very brief introduction to wavelet analysis is included in Chapter 10. Although the pyramid algorithms for wavelet decompositions and reconstructions are quite different from the Kalman filtering al gorithms, they can also be applied to time-domain filtering, and it is hoped that splines and wavelets can be incorporated with Kalman filtering in the near future. College Station and Houston Charles K. Chui September 1990 Guanrong Chen Preface to the First Edition Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear, unbiased, and min imum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time. It has been widely used in many areas of industrial and government applications such as video and laser tracking systems, satellite navigation, ballistic missile trajectory estimation, radar, and fire control. With the recent development of high-speed computers, the Kalman filter has become more use ful even for very complicated real-time applications.

Nonlinear Estimation

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Author : Shovan Bhaumik
language : en
Publisher: CRC Press
Release Date : 2019-07-24

Nonlinear Estimation written by Shovan Bhaumik and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-24 with Mathematics categories.

Nonlinear Estimation: Methods and Applications with Deterministic Sample Points focusses on a comprehensive treatment of deterministic sample point filters (also called Gaussian filters) and their variants for nonlinear estimation problems, for which no closed-form solution is available in general. Gaussian filters are becoming popular with the designers due to their ease of implementation and real time execution even on inexpensive or legacy hardware. The main purpose of the book is to educate the reader about a variety of available nonlinear estimation methods so that the reader can choose the right method for a real life problem, adapt or modify it where necessary and implement it. The book can also serve as a core graduate text for a course on state estimation. The book starts from the basic conceptual solution of a nonlinear estimation problem and provides an in depth coverage of (i) various Gaussian filters such as the unscented Kalman filter, cubature and quadrature based filters, Gauss-Hermite filter and their variants and (ii) Gaussian sum filter, in both discrete and continuous-discrete domain. Further, a brief description of filters for randomly delayed measurement and two case-studies are also included. Features: The book covers all the important Gaussian filters, including filters with randomly delayed measurements. Numerical simulation examples with detailed matlab code are provided for most algorithms so that beginners can verify their understanding. Two real world case studies are included: (i) underwater passive target tracking, (ii) ballistic target tracking. The style of writing is suitable for engineers and scientists. The material of the book is presented with the emphasis on key ideas, underlying assumptions, algorithms, and properties. The book combines rigorous mathematical treatment with matlab code, algorithm listings, flow charts and detailed case studies to deepen understanding.

Introduction And Implementations Of The Kalman Filter

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Author : Felix Govaers
language : en
Publisher: BoD – Books on Demand
Release Date : 2019-05-22

Introduction And Implementations Of The Kalman Filter written by Felix Govaers and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-22 with Computers categories.

Sensor data fusion is the process of combining error-prone, heterogeneous, incomplete, and ambiguous data to gather a higher level of situational awareness. In principle, all living creatures are fusing information from their complementary senses to coordinate their actions and to detect and localize danger. In sensor data fusion, this process is transferred to electronic systems, which rely on some "awareness" of what is happening in certain areas of interest. By means of probability theory and statistics, it is possible to model the relationship between the state space and the sensor data. The number of ingredients of the resulting Kalman filter is limited, but its applications are not.

Data Assimilation

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Author : Geir Evensen
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-12-22

Data Assimilation written by Geir Evensen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-12-22 with Science categories.

This book reviews popular data-assimilation methods, such as weak and strong constraint variational methods, ensemble filters and smoothers. The author shows how different methods can be derived from a common theoretical basis, as well as how they differ or are related to each other, and which properties characterize them, using several examples. Readers will appreciate the included introductory material and detailed derivations in the text, and a supplemental web site.

The Oxford Handbook Of Economic Forecasting

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Author : Michael P. Clements
language : en
Publisher: Oxford University Press
Release Date : 2011-06-29

The Oxford Handbook Of Economic Forecasting written by Michael P. Clements and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-29 with Business & Economics categories.

This Handbook provides up-to-date coverage of both new and well-established fields in the sphere of economic forecasting. The chapters are written by world experts in their respective fields, and provide authoritative yet accessible accounts of the key concepts, subject matter, and techniques in a number of diverse but related areas. It covers the ways in which the availability of ever more plentiful data and computational power have been used in forecasting, in terms of the frequency of observations, the number of variables, and the use of multiple data vintages. Greater data availability has been coupled with developments in statistical theory and economic analysis to allow more elaborate and complicated models to be entertained; the volume provides explanations and critiques of these developments. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models, as well as models for handling data observed at mixed frequencies, high-frequency data, multiple data vintages, methods for forecasting when there are structural breaks, and how breaks might be forecast. Also covered are areas which are less commonly associated with economic forecasting, such as climate change, health economics, long-horizon growth forecasting, and political elections. Econometric forecasting has important contributions to make in these areas along with how their developments inform the mainstream.