Particle Filters For Random Set Models

DOWNLOAD

Download Particle Filters For Random Set Models PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Particle Filters For Random Set Models book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Particle Filters For Random Set Models

DOWNLOAD
Author : Branko Ristic
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-15

Particle Filters For Random Set Models written by Branko Ristic 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 2013-04-15 with Technology & Engineering categories.

This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. Although the resulting algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.

Random Finite Sets For Robot Mapping Slam

DOWNLOAD
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.

An Introduction To Sequential Monte Carlo

DOWNLOAD
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.

Feynman Kac Formulae

DOWNLOAD
Author : Pierre Del Moral
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-03-30

Feynman Kac Formulae written by Pierre Del Moral 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 2004-03-30 with Mathematics categories.

This text takes readers in a clear and progressive format from simple to recent and advanced topics in pure and applied probability such as contraction and annealed properties of non-linear semi-groups, functional entropy inequalities, empirical process convergence, increasing propagations of chaos, central limit, and Berry Esseen type theorems as well as large deviation principles for strong topologies on path-distribution spaces. Topics also include a body of powerful branching and interacting particle methods.

Sequential Monte Carlo Methods In Practice

DOWNLOAD
Author : Arnaud Doucet
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09

Sequential Monte Carlo Methods In Practice written by Arnaud Doucet 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 2013-03-09 with Mathematics categories.

Monte Carlo methods are revolutionising the on-line analysis of data in fields as diverse as financial modelling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survial of the fittest, have made it possible to solve numerically many complex, non-standarard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modelling, neural networks,optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practicioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris- XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning.

Bayesian Filtering And Smoothing

DOWNLOAD
Author : Simo Särkkä
language : en
Publisher: Cambridge University Press
Release Date : 2013-09-05

Bayesian Filtering And Smoothing written by Simo Särkkä 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 2013-09-05 with Computers categories.

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

DOWNLOAD
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.

Particle Filters For Robot Navigation

DOWNLOAD
Author : Cyrill Stachniss
language : en
Publisher:
Release Date : 2014

Particle Filters For Robot Navigation written by Cyrill Stachniss and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Autonomous robots categories.

Autonomous navigation is an essential capability for mobile robots. In order to operate robustly, a robot needs to know what the environment looks like, where it is in its environment, and how to navigate in it. This work summarizes approaches that address these three problems and that use particle filters as their main underlying model for representing beliefs. We illustrate that these filters are powerful tools that can robustly estimate the state of the robot and its environment and that it is also well-suited to make decisions about how to navigate in order to minimize the uncertainty of the joint belief about the robot's position and the state of the environment.

Theory Of Random Sets

DOWNLOAD
Author : Ilya Molchanov
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-05-11

Theory Of Random Sets written by Ilya Molchanov 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 2005-05-11 with Mathematics categories.

This is the first systematic exposition of random sets theory since Matheron (1975), with full proofs, exhaustive bibliographies and literature notes Interdisciplinary connections and applications of random sets are emphasized throughout the book An extensive bibliography in the book is available on the Web at http://liinwww.ira.uka.de/bibliography/math/random.closed.sets.html, and is accompanied by a search engine

Optimization Under Uncertainty With Applications To Aerospace Engineering

DOWNLOAD
Author : Massimiliano Vasile
language : en
Publisher: Springer Nature
Release Date : 2021-02-15

Optimization Under Uncertainty With Applications To Aerospace Engineering written by Massimiliano Vasile and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-15 with Science categories.

In an expanding world with limited resources, optimization and uncertainty quantification have become a necessity when handling complex systems and processes. This book provides the foundational material necessary for those who wish to embark on advanced research at the limits of computability, collecting together lecture material from leading experts across the topics of optimization, uncertainty quantification and aerospace engineering. The aerospace sector in particular has stringent performance requirements on highly complex systems, for which solutions are expected to be optimal and reliable at the same time. The text covers a wide range of techniques and methods, from polynomial chaos expansions for uncertainty quantification to Bayesian and Imprecise Probability theories, and from Markov chains to surrogate models based on Gaussian processes. The book will serve as a valuable tool for practitioners, researchers and PhD students.