Restricted Kalman Filtering
Restricted Kalman Filtering

Author: Adrian Pizzinga

Publisher: Springer Science & Business Media

Published: 2012-07-25

Total Pages: 66

ISBN-13: 1461447380

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​​​​​​​​ ​In statistics, the Kalman filter is a mathematical method whose purpose is to use a series of measurements observed over time, containing random variations and other inaccuracies, and produce estimates that tend to be closer to the true unknown values than those that would be based on a single measurement alone. This Brief offers developments on Kalman filtering subject to general linear constraints. There are essentially three types of contributions: new proofs for results already established; new results within the subject; and applications in investment analysis and macroeconomics, where the proposed methods are illustrated and evaluated. The Brief has a short chapter on linear state space models and the Kalman filter, aiming to make the book self-contained and to give a quick reference to the reader (notation and terminology). The prerequisites would be a contact with time series analysis in the level of Hamilton (1994) or Brockwell & Davis (2002) and also with linear state models and the Kalman filter – each of these books has a chapter entirely dedicated to the subject. The book is intended for graduate students, researchers and practitioners in statistics (specifically: time series analysis and econometrics).

Beyond the Kalman Filter: Particle Filters for Tracking Applications
Beyond the Kalman Filter: Particle Filters for Tracking Applications

Author: Branko Ristic

Publisher: Artech House

Published: 2003-12-01

Total Pages: 328

ISBN-13: 9781580538510

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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.

Kalman Filtering
Kalman Filtering

Author: Mohinder S. Grewal

Publisher: Wiley-Interscience

Published: 2001-01-16

Total Pages: 424

ISBN-13:

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Disk contains: Demonstation programs and source code in MATLAB for algorithms in text.

Kalman Filters
Kalman Filters

Author: Ginalber Luiz Serra

Publisher: BoD – Books on Demand

Published: 2018-02-21

Total Pages: 315

ISBN-13: 9535138278

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This book presents recent issues on theory and practice of Kalman filters, with a comprehensive treatment of a selected number of concepts, techniques, and advanced applications. From an interdisciplinary point of view, the contents from each chapter bring together an international scientific community to discuss the state of the art on Kalman filter-based methodologies for adaptive/distributed filtering, optimal estimation, dynamic prediction, nonstationarity, robot navigation, global navigation satellite systems, moving object tracking, optical communication systems, and active power filters, among others. The theoretical and methodological foundations combined with extensive experimental explanation make this book a reference suitable for students, practicing engineers, and researchers in sciences and engineering.

Kalman Filtering
Kalman Filtering

Author: Mohinder S. Grewal

Publisher: John Wiley & Sons

Published: 2015-02-02

Total Pages: 639

ISBN-13: 111898496X

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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.

Kalman Filtering
Kalman Filtering

Author: Charles K. Chui

Publisher: Springer Science & Business Media

Published: 2009

Total Pages: 241

ISBN-13: 3540878483

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This book presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method and an indirect method.

Kalman Filter
Kalman Filter

Author: Víctor M. Moreno

Publisher: BoD – Books on Demand

Published: 2009-04-01

Total Pages: 608

ISBN-13: 9533070005

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The aim of this book is to provide an overview of recent developments in Kalman filter theory and their applications in engineering and scientific fields. The book is divided into 24 chapters and organized in five blocks corresponding to recent advances in Kalman filtering theory, applications in medical and biological sciences, tracking and positioning systems, electrical engineering and, finally, industrial processes and communication networks.