Convergence Of The Extended Kalman Filter

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Directions in Mathematical Systems Theory and Optimization

Author : Anders Rantzer
Publisher : Springer Science & Business Media
ISBN 13 : 3540000658
Total Pages : 392 pages
Book Rating : 4.5/5 (4 download)

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Book Synopsis Directions in Mathematical Systems Theory and Optimization by : Anders Rantzer

Download or read book Directions in Mathematical Systems Theory and Optimization written by Anders Rantzer and published by Springer Science & Business Media. This book was released on 2002-11-05 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: For more than three decades, Anders Lindquist has delivered fundamental cont- butions to the ?elds of systems, signals and control. Throughout this period, four themes can perhaps characterize his interests: Modeling, estimation and ?ltering, feedback and robust control. His contributions to modeling include seminal work on the role of splitting subspaces in stochastic realization theory, on the partial realization problem for both deterministic and stochastic systems, on the solution of the rational covariance extension problem and on system identi?cation. His contributions to ?ltering and estimation include the development of fast ?ltering algorithms, leading to a nonlinear dynamical system which computes spectral factors in its steady state, and which provide an alternate, linear in the dimension of the state space, to computing the Kalman gain from a matrix Riccati equation. His further research on the phase portrait of this dynamical system gave a better understanding of when the Kalman ?lter will converge, answering an open question raised by Kalman. While still a student he established the separation principle for stochastic function differential equations, including some fundamental work on optimal control for stochastic systems with time lags. He continued his interest in feedback control by deriving optimal and robust control feedback laws for suppressing the effects of harmonic disturbances. Moreover, his recent work on a complete parameterization of all rational solutions to the Nevanlinna-Pick problem is providing a new approach to robust control design.

An Introduction to the Extended Kalman Filter

Author : Matthias Holland
Publisher : Nova Science Publishers
ISBN 13 : 9781536188844
Total Pages : 115 pages
Book Rating : 4.1/5 (888 download)

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Book Synopsis An Introduction to the Extended Kalman Filter by : Matthias Holland

Download or read book An Introduction to the Extended Kalman Filter written by Matthias Holland and published by Nova Science Publishers. This book was released on 2020 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: "An Introduction to the Extended Kalman Filter first presents a study wherein a two-stage approach for the estimation of a spacecraft's position and velocity using single station antenna tracking data is proposed. Since the Kalman filter and its variants are widely used for estimation in diverse domains, the authors also present a review of fault detection, diagnosis and fault tolerant control of descriptor/differential algebraic equation systems specifically focused on the Kalman filter and its variants. The closing contribution provides insight into the intrinsic convergence of the extended Kalman filter when operated in the stochastic frame for the class of systems and outputs considered"--

New Trends in Nonlinear Dynamics and Control, and their Applications

Author : Wei Kang
Publisher : Springer Science & Business Media
ISBN 13 : 9783540404743
Total Pages : 384 pages
Book Rating : 4.4/5 (47 download)

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Book Synopsis New Trends in Nonlinear Dynamics and Control, and their Applications by : Wei Kang

Download or read book New Trends in Nonlinear Dynamics and Control, and their Applications written by Wei Kang and published by Springer Science & Business Media. This book was released on 2003-09-16 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: A selection of papers exploring a wide spectrum of new trends in nonlinear dynamics and control, such as bifurcation control, state estimation and reconstruction, analysis of behavior and stabilities, dynamics of nonlinear neural network models, and numerical algorithms. The papers focus on new ideas and the latest developments in both theoretical and applied research topics of nonlinear control. Because many of the authors are leading researchers in their own fields, the papers presented in this volume reflect the state of the art in the areas of nonlinear dynamics and control. Many of the papers in this volume were first presented at the highly succesful ''Symposium on New Trends in Nonlinear Dynamics and Control, and Their Applications,'' held October 18-19, 2002, in Monterey, California.

Introduction and Implementations of the Kalman Filter

Author : Felix Govaers
Publisher : BoD – Books on Demand
ISBN 13 : 1838805362
Total Pages : 130 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Introduction and Implementations of the Kalman Filter by : Felix Govaers

Download or read book Introduction and Implementations of the Kalman Filter written by Felix Govaers and published by BoD – Books on Demand. This book was released on 2019-05-22 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Stochastic Systems and State Estimation

Author : Terrence P. McGarty
Publisher : Wiley-Interscience
ISBN 13 :
Total Pages : 426 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Stochastic Systems and State Estimation by : Terrence P. McGarty

Download or read book Stochastic Systems and State Estimation written by Terrence P. McGarty and published by Wiley-Interscience. This book was released on 1974 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Optimal State Estimation

Author : Dan Simon
Publisher : John Wiley & Sons
ISBN 13 : 0470045337
Total Pages : 554 pages
Book Rating : 4.4/5 (7 download)

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Book Synopsis Optimal State Estimation by : Dan Simon

Download or read book Optimal State Estimation written by Dan Simon and published by John Wiley & Sons. This book was released on 2006-06-19 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.

Kalman Filtering and Neural Networks

Author : Simon Haykin
Publisher : John Wiley & Sons
ISBN 13 : 047146421X
Total Pages : 302 pages
Book Rating : 4.4/5 (714 download)

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Book Synopsis Kalman Filtering and Neural Networks by : Simon Haykin

Download or read book Kalman Filtering and Neural Networks written by Simon Haykin and published by John Wiley & Sons. This book was released on 2004-03-24 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover: An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF) Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm The unscented Kalman filter Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.

1999 Flight Mechanics Symposium

Author : John P. Lynch
Publisher :
ISBN 13 :
Total Pages : 552 pages
Book Rating : 4.:/5 (317 download)

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Book Synopsis 1999 Flight Mechanics Symposium by : John P. Lynch

Download or read book 1999 Flight Mechanics Symposium written by John P. Lynch and published by . This book was released on 1999 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Data Assimilation

Author : Geir Evensen
Publisher : Springer Science & Business Media
ISBN 13 : 3540383018
Total Pages : 285 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Data Assimilation by : Geir Evensen

Download or read book Data Assimilation written by Geir Evensen and published by Springer Science & Business Media. This book was released on 2006-12-22 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Kalman Filtering

Author : Mohinder S. Grewal
Publisher : John Wiley & Sons
ISBN 13 : 111898496X
Total Pages : 639 pages
Book Rating : 4.1/5 (189 download)

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Book Synopsis Kalman Filtering by : Mohinder S. Grewal

Download or read book Kalman Filtering written by Mohinder S. Grewal and published by John Wiley & Sons. This book was released on 2015-02-02 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: The definitive textbook and professional reference on Kalman Filtering – fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.

Flight Mechanics Symposium

Author :
Publisher :
ISBN 13 :
Total Pages : 562 pages
Book Rating : 4.0/5 ( download)

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Book Synopsis Flight Mechanics Symposium by :

Download or read book Flight Mechanics Symposium written by and published by . This book was released on 1999 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Directions in Mathematical Systems Theory and Optimization

Author : Anders Rantzer
Publisher : Springer
ISBN 13 : 3540361065
Total Pages : 392 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Directions in Mathematical Systems Theory and Optimization by : Anders Rantzer

Download or read book Directions in Mathematical Systems Theory and Optimization written by Anders Rantzer and published by Springer. This book was released on 2003-07-01 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: For more than three decades, Anders Lindquist has delivered fundamental cont- butions to the ?elds of systems, signals and control. Throughout this period, four themes can perhaps characterize his interests: Modeling, estimation and ?ltering, feedback and robust control. His contributions to modeling include seminal work on the role of splitting subspaces in stochastic realization theory, on the partial realization problem for both deterministic and stochastic systems, on the solution of the rational covariance extension problem and on system identi?cation. His contributions to ?ltering and estimation include the development of fast ?ltering algorithms, leading to a nonlinear dynamical system which computes spectral factors in its steady state, and which provide an alternate, linear in the dimension of the state space, to computing the Kalman gain from a matrix Riccati equation. His further research on the phase portrait of this dynamical system gave a better understanding of when the Kalman ?lter will converge, answering an open question raised by Kalman. While still a student he established the separation principle for stochastic function differential equations, including some fundamental work on optimal control for stochastic systems with time lags. He continued his interest in feedback control by deriving optimal and robust control feedback laws for suppressing the effects of harmonic disturbances. Moreover, his recent work on a complete parameterization of all rational solutions to the Nevanlinna-Pick problem is providing a new approach to robust control design.

Bayesian Filtering and Smoothing

Author : Simo Särkkä
Publisher : Cambridge University Press
ISBN 13 : 110703065X
Total Pages : 255 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Bayesian Filtering and Smoothing by : Simo Särkkä

Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2013-09-05 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.

Beyond the Kalman Filter: Particle Filters for Tracking Applications

Author : Branko Ristic
Publisher : Artech House
ISBN 13 : 9781580538510
Total Pages : 328 pages
Book Rating : 4.5/5 (385 download)

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Book Synopsis Beyond the Kalman Filter: Particle Filters for Tracking Applications by : Branko Ristic

Download or read book Beyond the Kalman Filter: Particle Filters for Tracking Applications written by Branko Ristic and published by Artech House. This book was released on 2003-12-01 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian problems. To solve problems beyond this restricted class, particle filters are proving to be dependable methods for stochastic dynamic estimation. Packed with 867 equations, this cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defense surveillance systems, and examines defense-related applications of particle filters to nonlinear and non-Gaussian problems. With this hands-on guide, you can develop more accurate and reliable nonlinear filter designs and more precisely predict the performance of these designs. You can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bi-static radar tracking, passive ranging (bearings-only tracking) of maneuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.

Optimal Filtering

Author : Brian D. O. Anderson
Publisher : Courier Corporation
ISBN 13 : 0486136892
Total Pages : 370 pages
Book Rating : 4.4/5 (861 download)

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Book Synopsis Optimal Filtering by : Brian D. O. Anderson

Download or read book Optimal Filtering written by Brian D. O. Anderson and published by Courier Corporation. This book was released on 2012-05-23 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graduate-level text extends studies of signal processing, particularly regarding communication systems and digital filtering theory. Topics include filtering, linear systems, and estimation; discrete-time Kalman filter; time-invariant filters; more. 1979 edition.

Nonlinear Programming

Author : Dimitri Bertsekas
Publisher : Athena Scientific
ISBN 13 : 1886529051
Total Pages : 1100 pages
Book Rating : 4.8/5 (865 download)

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Book Synopsis Nonlinear Programming by : Dimitri Bertsekas

Download or read book Nonlinear Programming written by Dimitri Bertsekas and published by Athena Scientific. This book was released on 2016-09-01 with total page 1100 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and accessible presentation of algorithms for solving continuous optimization problems. It relies on rigorous mathematical analysis, but also aims at an intuitive exposition that makes use of visualization where possible. It places particular emphasis on modern developments, and their widespread applications in fields such as large-scale resource allocation problems, signal processing, and machine learning. The 3rd edition brings the book in closer harmony with the companion works Convex Optimization Theory (Athena Scientific, 2009), Convex Optimization Algorithms (Athena Scientific, 2015), Convex Analysis and Optimization (Athena Scientific, 2003), and Network Optimization (Athena Scientific, 1998). These works are complementary in that they deal primarily with convex, possibly nondifferentiable, optimization problems and rely on convex analysis. By contrast the nonlinear programming book focuses primarily on analytical and computational methods for possibly nonconvex differentiable problems. It relies primarily on calculus and variational analysis, yet it still contains a detailed presentation of duality theory and its uses for both convex and nonconvex problems. This on-line edition contains detailed solutions to all the theoretical book exercises. Among its special features, the book: Provides extensive coverage of iterative optimization methods within a unifying framework Covers in depth duality theory from both a variational and a geometric point of view Provides a detailed treatment of interior point methods for linear programming Includes much new material on a number of topics, such as proximal algorithms, alternating direction methods of multipliers, and conic programming Focuses on large-scale optimization topics of much current interest, such as first order methods, incremental methods, and distributed asynchronous computation, and their applications in machine learning, signal processing, neural network training, and big data applications Includes a large number of examples and exercises Was developed through extensive classroom use in first-year graduate courses

Multi-Sensor Information Fusion

Author : Xue-Bo Jin
Publisher : MDPI
ISBN 13 : 3039283022
Total Pages : 602 pages
Book Rating : 4.0/5 (392 download)

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Book Synopsis Multi-Sensor Information Fusion by : Xue-Bo Jin

Download or read book Multi-Sensor Information Fusion written by Xue-Bo Jin and published by MDPI. This book was released on 2020-03-23 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes papers from the section “Multisensor Information Fusion”, from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technologies, estimation and filtering, and the latest research, artificial intelligence involving deep learning.