Bayesian Estimation And Tracking

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Bayesian Estimation and Tracking

Author : Anton J. Haug
Publisher : John Wiley & Sons
ISBN 13 : 1118287800
Total Pages : 400 pages
Book Rating : 4.1/5 (182 download)

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Book Synopsis Bayesian Estimation and Tracking by : Anton J. Haug

Download or read book Bayesian Estimation and Tracking written by Anton J. Haug and published by John Wiley & Sons. This book was released on 2012-05-29 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of eeach estimation method and goes on to use illustrative and detailed step-by-step instructions for each method that makes coding of the tracking filter simple and easy to understand. Case studies are employed to showcase applications of the discussed topics. In addition, the book supplies block diagrams for each algorithm, allowing readers to develop their own MATLAB® toolbox of estimation methods. Bayesian Estimation and Tracking is an excellent book for courses on estimation and tracking methods at the graduate level. The book also serves as a valuable reference for research scientists, mathematicians, and engineers seeking a deeper understanding of the topics.

Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking

Author : Harry L. Van Trees
Publisher : Wiley-IEEE Press
ISBN 13 : 9780470120958
Total Pages : 951 pages
Book Rating : 4.1/5 (29 download)

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Book Synopsis Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking by : Harry L. Van Trees

Download or read book Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking written by Harry L. Van Trees and published by Wiley-IEEE Press. This book was released on 2007-08-31 with total page 951 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive development of Bayesian Bounds for parameter estimation and nonlinear filtering/tracking Bayesian estimation plays a central role in many signal processing problems encountered in radar, sonar, communications, seismology, and medical diagnosis. There are often highly nonlinear problems for which analytic evaluation of the exact performance is intractable. A widely used technique is to find bounds on the performance of any estimator and compare the performance of various estimators to these bounds. This book provides a comprehensive overview of the state of the art in Bayesian Bounds. It addresses two related problems: the estimation of multiple parameters based on noisy measurements and the estimation of random processes, either continuous or discrete, based on noisy measurements. An extensive introductory chapter provides an overview of Bayesian estimation and the interrelationship and applicability of the various Bayesian Bounds for both static parameters and random processes. It provides the context for the collection of papers that are included. This book will serve as a comprehensive reference for engineers and statisticians interested in both theory and application. It is also suitable as a text for a graduate seminar or as a supplementary reference for an estimation theory course.

Recursive Bayesian Estimation

Author : Niclas Bergman
Publisher :
ISBN 13 : 9789172194731
Total Pages : 204 pages
Book Rating : 4.1/5 (947 download)

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Book Synopsis Recursive Bayesian Estimation by : Niclas Bergman

Download or read book Recursive Bayesian Estimation written by Niclas Bergman and published by . This book was released on 1999 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Bayesian Estimation For Tracking Of Spiraling Reentry Vehicles

Author : Juan Esteban Tapiero Bernal
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (881 download)

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Book Synopsis Bayesian Estimation For Tracking Of Spiraling Reentry Vehicles by : Juan Esteban Tapiero Bernal

Download or read book Bayesian Estimation For Tracking Of Spiraling Reentry Vehicles written by Juan Esteban Tapiero Bernal and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a development of a physics-based dynamics model of a spiraling atmospheric reentry vehicle. An analysis of the trajectory characteristics, using elements from differential geometry lead to a relationship of the state of the vehicle to the spiraling of motion. The Bayesian estimation framework for nonlinear systems is introduced showing the theoretical basis of the estimation techniques. Two estimation algorithms, extended Kalman filter and particle filter are presented, their mathematical formulation and implementation characteristics. Different trajectories that can be represented by the model are introduced and analyzed, showing the spiraling behavior that can be described by the model. The extended Kalman filter and particle filter are compared in the ability to estimate the states and spiraling characteristics, with successful results for both techniques inside one standard deviation. In general superior performance was shown by the particle filter, which estimated the torsion with an error 10 orders of magnitude smaller.

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.

Bayesian Multiple Target Tracking

Author : Lawrence D. Stone
Publisher : Artech House Radar Library (Ha
ISBN 13 :
Total Pages : 362 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Bayesian Multiple Target Tracking by : Lawrence D. Stone

Download or read book Bayesian Multiple Target Tracking written by Lawrence D. Stone and published by Artech House Radar Library (Ha. This book was released on 1999 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get the solutions to your most challenging tracking problems with this up-to-date resource. Using the Bayesian inference framework, the book helps you design and develop mathematically sound algorithms for dealing with tracking problems involving multiple targets, multiple sensors, and multiple platforms. The book shows you how non-linear Multiple Hypothesis Tracking and the Theory of Unified Tracking are successful methods when multiple target tracking must be performed without contacts or association.

Introduction to Bayesian Tracking and Particle Filters

Author : Lawrence D. Stone
Publisher : Springer Nature
ISBN 13 : 3031322428
Total Pages : 124 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Introduction to Bayesian Tracking and Particle Filters by : Lawrence D. Stone

Download or read book Introduction to Bayesian Tracking and Particle Filters written by Lawrence D. Stone and published by Springer Nature. This book was released on 2023-05-31 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a quick but insightful introduction to Bayesian tracking and particle filtering for a person who has some background in probability and statistics and wishes to learn the basics of single-target tracking. It also introduces the reader to multiple target tracking by presenting useful approximate methods that are easy to implement compared to full-blown multiple target trackers. The book presents the basic concepts of Bayesian inference and demonstrates the power of the Bayesian method through numerous applications of particle filters to tracking and smoothing problems. It emphasizes target motion models that incorporate knowledge about the target’s behavior in a natural fashion rather than assumptions made for mathematical convenience. The background provided by this book allows a person to quickly become a productive member of a project team using Bayesian filtering and to develop new methods and techniques for problems the team may face.

Stochastic Bayesian Estimation Using Efficient Particle Filters for Vehicle Tracking Applications

Author : Giorgos Kravaritis
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (66 download)

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Book Synopsis Stochastic Bayesian Estimation Using Efficient Particle Filters for Vehicle Tracking Applications by : Giorgos Kravaritis

Download or read book Stochastic Bayesian Estimation Using Efficient Particle Filters for Vehicle Tracking Applications written by Giorgos Kravaritis and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Stochastic Bayesian Estimation Using Efficient Particle Filters for Vehicle Tracking Applications

Author : Giorgos Kravaritis
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (66 download)

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Book Synopsis Stochastic Bayesian Estimation Using Efficient Particle Filters for Vehicle Tracking Applications by : Giorgos Kravaritis

Download or read book Stochastic Bayesian Estimation Using Efficient Particle Filters for Vehicle Tracking Applications written by Giorgos Kravaritis and published by . This book was released on 2006 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Glimpsed Periodicity Features and Recursive Bayesian Estimation for Modeling Attentive Voice Tracking

Author : Joanna Luberadzka
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (119 download)

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Book Synopsis Glimpsed Periodicity Features and Recursive Bayesian Estimation for Modeling Attentive Voice Tracking by : Joanna Luberadzka

Download or read book Glimpsed Periodicity Features and Recursive Bayesian Estimation for Modeling Attentive Voice Tracking written by Joanna Luberadzka and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Bayesian Analysis for Population Ecology

Author : Ruth King
Publisher : CRC Press
ISBN 13 : 1439811881
Total Pages : 457 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Bayesian Analysis for Population Ecology by : Ruth King

Download or read book Bayesian Analysis for Population Ecology written by Ruth King and published by CRC Press. This book was released on 2009-10-30 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emphasizing model choice and model averaging, this book presents up-to-date Bayesian methods for analyzing complex ecological data. It provides a basic introduction to Bayesian methods that assumes no prior knowledge. The book includes detailed descriptions of methods that deal with covariate data and covers techniques at the forefront of research, such as model discrimination and model averaging. Leaders in the statistical ecology field, the authors apply the theory to a wide range of actual case studies and illustrate the methods using WinBUGS and R. The computer programs and full details of the data sets are available on the book's website.

Bayesian Filtering and Smoothing

Author : Simo Särkkä
Publisher : Cambridge University Press
ISBN 13 : 1108912303
Total Pages : 438 pages
Book Rating : 4.1/5 (89 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 2023-05-31 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this accessible text presents a unified Bayesian treatment of state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models. The book focuses on discrete-time state space models and carefully introduces fundamental aspects related to optimal filtering and smoothing. In particular, it covers a range of efficient non-linear Gaussian filtering and smoothing algorithms, as well as Monte Carlo-based algorithms. This updated edition features new chapters on constructing state space models of practical systems, the discretization of continuous-time state space models, Gaussian filtering by enabling approximations, posterior linearization filtering, and the corresponding smoothers. Coverage of key topics is expanded, including extended Kalman filtering and smoothing, and parameter estimation. The book's practical, algorithmic approach assumes only modest mathematical prerequisites, suitable for graduate and advanced undergraduate students. Many examples are included, with Matlab and Python code available online, enabling readers to implement algorithms in their own projects.

Characteristics of Bayesian Multiple Model Adaptive Estimation for Tracking Airborne Targets

Author : Allan S. Netzer (CAPT, USAF.)
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (132 download)

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Book Synopsis Characteristics of Bayesian Multiple Model Adaptive Estimation for Tracking Airborne Targets by : Allan S. Netzer (CAPT, USAF.)

Download or read book Characteristics of Bayesian Multiple Model Adaptive Estimation for Tracking Airborne Targets written by Allan S. Netzer (CAPT, USAF.) and published by . This book was released on 1985 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Introduction to Bayesian Estimation and Copula Models of Dependence

Author : Arkady Shemyakin
Publisher : John Wiley & Sons
ISBN 13 : 1118959035
Total Pages : 350 pages
Book Rating : 4.1/5 (189 download)

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Book Synopsis Introduction to Bayesian Estimation and Copula Models of Dependence by : Arkady Shemyakin

Download or read book Introduction to Bayesian Estimation and Copula Models of Dependence written by Arkady Shemyakin and published by John Wiley & Sons. This book was released on 2017-02-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents an introduction to Bayesian statistics, presents an emphasis on Bayesian methods (prior and posterior), Bayes estimation, prediction, MCMC,Bayesian regression, and Bayesian analysis of statistical modelsof dependence, and features a focus on copulas for risk management Introduction to Bayesian Estimation and Copula Models of Dependence emphasizes the applications of Bayesian analysis to copula modeling and equips readers with the tools needed to implement the procedures of Bayesian estimation in copula models of dependence. This book is structured in two parts: the first four chapters serve as a general introduction to Bayesian statistics with a clear emphasis on parametric estimation and the following four chapters stress statistical models of dependence with a focus of copulas. A review of the main concepts is discussed along with the basics of Bayesian statistics including prior information and experimental data, prior and posterior distributions, with an emphasis on Bayesian parametric estimation. The basic mathematical background of both Markov chains and Monte Carlo integration and simulation is also provided. The authors discuss statistical models of dependence with a focus on copulas and present a brief survey of pre-copula dependence models. The main definitions and notations of copula models are summarized followed by discussions of real-world cases that address particular risk management problems. In addition, this book includes: • Practical examples of copulas in use including within the Basel Accord II documents that regulate the world banking system as well as examples of Bayesian methods within current FDA recommendations • Step-by-step procedures of multivariate data analysis and copula modeling, allowing readers to gain insight for their own applied research and studies • Separate reference lists within each chapter and end-of-the-chapter exercises within Chapters 2 through 8 • A companion website containing appendices: data files and demo files in Microsoft® Office Excel®, basic code in R, and selected exercise solutions Introduction to Bayesian Estimation and Copula Models of Dependence is a reference and resource for statisticians who need to learn formal Bayesian analysis as well as professionals within analytical and risk management departments of banks and insurance companies who are involved in quantitative analysis and forecasting. This book can also be used as a textbook for upper-undergraduate and graduate-level courses in Bayesian statistics and analysis. ARKADY SHEMYAKIN, PhD, is Professor in the Department of Mathematics and Director of the Statistics Program at the University of St. Thomas. A member of the American Statistical Association and the International Society for Bayesian Analysis, Dr. Shemyakin's research interests include informationtheory, Bayesian methods of parametric estimation, and copula models in actuarial mathematics, finance, and engineering. ALEXANDER KNIAZEV, PhD, is Associate Professor and Head of the Department of Mathematics at Astrakhan State University in Russia. Dr. Kniazev's research interests include representation theory of Lie algebras and finite groups, mathematical statistics, econometrics, and financial mathematics.

Bayesian Real-Time System Identification

Author : Ke Huang
Publisher : Springer Nature
ISBN 13 : 9819905931
Total Pages : 286 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Bayesian Real-Time System Identification by : Ke Huang

Download or read book Bayesian Real-Time System Identification written by Ke Huang and published by Springer Nature. This book was released on 2023-03-20 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces some recent developments in Bayesian real-time system identification. It contains two different perspectives on data processing for system identification, namely centralized and distributed. A centralized Bayesian identification framework is presented to address challenging problems of real-time parameter estimation, which covers outlier detection, system, and noise parameters tracking. Besides, real-time Bayesian model class selection is introduced to tackle model misspecification problem. On the other hand, a distributed Bayesian identification framework is presented to handle asynchronous data and multiple outlier corrupted data. This book provides sufficient background to follow Bayesian methods for solving real-time system identification problems in civil and other engineering disciplines. The illustrative examples allow the readers to quickly understand the algorithms and associated applications. This book is intended for graduate students and researchers in civil and mechanical engineering. Practitioners can also find useful reference guide for solving engineering problems.

Characteristics of Bayesian Multiple Model Adaptive Estimation for Tracking Airborne Targets

Author : Allan S. Netzer
Publisher :
ISBN 13 :
Total Pages : 336 pages
Book Rating : 4.:/5 (227 download)

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Book Synopsis Characteristics of Bayesian Multiple Model Adaptive Estimation for Tracking Airborne Targets by : Allan S. Netzer

Download or read book Characteristics of Bayesian Multiple Model Adaptive Estimation for Tracking Airborne Targets written by Allan S. Netzer and published by . This book was released on 1985 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Previous studies at the Air Force Institute of Technology have led to the development of a multiple model adaptive filter (MMAF) tracking algorithm which provides significant improvements in tracker performance against highly-dynamic airborne targets over the currently used correlation trackers. A forward looking infra-red (FLIR) sensor is used to provide a target shape function to the tracking algorithm in the form of an 8 x 8 array of intensities projected onto a field of view (FOV). This target image measurement is correlated with an estimate of the target image a template, to produce linear offset pseudo-measurements from the center of the FOV, which are provided as measurements to a bank of linear Kalman filters, in the multiple model adaptive filtering (MMAF) structure. The output of the MMAF provides the state estimates used in pointing the FLIR sensor, and generating the new target image estimate. This study investigates the characteristics of this algorithm in order to evaluate its performance against various target scenarios. (Author).

Bayesian Signal Processing

Author : James V. Candy
Publisher : John Wiley & Sons
ISBN 13 : 1119125480
Total Pages : 712 pages
Book Rating : 4.1/5 (191 download)

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Book Synopsis Bayesian Signal Processing by : James V. Candy

Download or read book Bayesian Signal Processing written by James V. Candy and published by John Wiley & Sons. This book was released on 2016-06-20 with total page 712 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents the Bayesian approach to statistical signal processing for a variety of useful model sets This book aims to give readers a unified Bayesian treatment starting from the basics (Baye’s rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on “Sequential Bayesian Detection,” a new section on “Ensemble Kalman Filters” as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to “fill-in-the gaps” of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical “sanity testing” lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation/detection problems. The second edition of Bayesian Signal Processing features: “Classical” Kalman filtering for linear, linearized, and nonlinear systems; “modern” unscented and ensemble Kalman filters: and the “next-generation” Bayesian particle filters Sequential Bayesian detection techniques incorporating model-based schemes for a variety of real-world problems Practical Bayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics New case studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving MATLAB® notes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available Problem sets included to test readers’ knowledge and help them put their new skills into practice Bayesian Signal Processing, Second Edition is written for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems.