Beyond The Kalman Filter Particle Filters For Tracking Applications
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Beyond The Kalman Filter Particle Filters For Tracking Applications
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Author : Branko Ristic
language : en
Publisher: Artech House
Release Date : 2003-12-01
Beyond The Kalman Filter Particle Filters For Tracking Applications written by Branko Ristic and has been published by Artech House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-12-01 with Technology & Engineering categories.
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.
Beyond The Kalman Filter
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Author : Branko Ristic
language : en
Publisher: Artech House Publishers
Release Date : 2004-01
Beyond The Kalman Filter written by Branko Ristic and has been published by Artech House Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-01 with Technology & Engineering categories.
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. This cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defence surveillance systems, and examines defence-related applications of particle filters to nonlinear and non-Gaussian problems. 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 manoeuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.
Beyond The Kalman Filter Particle Filters For Tracking Applications
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Author : BRANCO. RISTIC
language : en
Publisher:
Release Date : 2004
Beyond The Kalman Filter Particle Filters For Tracking Applications written by BRANCO. RISTIC and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
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.
Estimation With Applications To Tracking And Navigation
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Author : Yaakov Bar-Shalom
language : en
Publisher: John Wiley & Sons
Release Date : 2004-03-22
Estimation With Applications To Tracking And Navigation written by Yaakov Bar-Shalom and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-03-22 with Technology & Engineering categories.
Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics. The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimation with a focus on applying these techniques to real systems. Other features include: * Problems that apply theoretical material to real-world applications * In-depth coverage of the Interacting Multiple Model (IMM) estimator * Companion DynaEst(TM) software for MATLAB(TM) implementation of Kalman filters and IMM estimators * Design guidelines for tracking filters Suitable for graduate engineering students and engineers working in remote sensors and tracking, Estimation with Applications to Tracking and Navigation provides expert coverage of this important area.
Kalman Filtering
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Author : Mohinder S. Grewal
language : en
Publisher: John Wiley & Sons
Release Date : 2015-02-02
Kalman Filtering written by Mohinder S. Grewal and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-02 with Technology & Engineering categories.
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.
Fundamentals Of Object Tracking
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Author :
language : en
Publisher: Cambridge University Press
Release Date : 2011-07-28
Fundamentals Of Object Tracking written by and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-28 with Mathematics categories.
Introduces object tracking algorithms from a unified, recursive Bayesian perspective, along with performance bounds and illustrative examples.
Random Finite Sets For Robot Mapping Slam
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Author : John Stephen Mullane
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-05-19
Random Finite Sets For Robot Mapping Slam written by John Stephen Mullane 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 2011-05-19 with Technology & Engineering categories.
The monograph written by John Mullane, Ba-Ngu Vo, Martin Adams and Ba-Tuong Vo is devoted to the field of autonomous robot systems, which have been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the problem of representing the environment and its uncertainty in terms of feature based maps. Random Finite Sets are adopted as the fundamental tool to represent a map, and a general framework is proposed for feature management, data association and state estimation. The approaches are tested in a number of experiments on both ground based and marine based facilities.
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.
An Introduction To Sequential Monte Carlo
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Author : Nicolas Chopin
language : en
Publisher: Springer Nature
Release Date : 2020-10-01
An Introduction To Sequential Monte Carlo written by Nicolas Chopin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-01 with Mathematics categories.
This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics. The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achieved by describing SMC algorithms as particular cases of a general framework, which involves concepts such as Feynman-Kac distributions, and tools such as importance sampling and resampling. This general framework is used consistently throughout the book. Extensive coverage is provided on sequential learning (filtering, smoothing) of state-space (hidden Markov) models, as this remains an important application of SMC methods. More recent applications, such as parameter estimation of these models (through e.g. particle Markov chain Monte Carlo techniques) and the simulation of challenging probability distributions (in e.g. Bayesian inference or rare-event problems), are also discussed. The book may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area. Each chapter includes a set of exercises for self-study, a comprehensive bibliography, and a “Python corner,” which discusses the practical implementation of the methods covered. In addition, the book comes with an open source Python library, which implements all the algorithms described in the book, and contains all the programs that were used to perform the numerical experiments.