Bayesian Estimation and Tracking

Author: Anton J. Haug
Publisher: John Wiley & Sons
ISBN: 1118287800
Format: PDF
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A practical approach to estimating and tracking dynamicsystems in real-worl applications Much of the literature on performing estimation for non-Gaussiansystems is short on practical methodology, while Gaussian methodsoften lack a cohesive derivation. Bayesian Estimation andTracking addresses the gap in the field on both accounts,providing readers with a comprehensive overview of methods forestimating both linear and nonlinear dynamic systems driven byGaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation andtracking, the book emphasizes the derivation of all trackingalgorithms within a Bayesian framework and describes effectivenumerical methods for evaluating density-weighted integrals,including linear and nonlinear Kalman filters for Gaussian-weightedintegrals and particle filters for non-Gaussian cases. The authorfirst emphasizes detailed derivations from first principles ofeeach estimation method and goes on to use illustrative anddetailed step-by-step instructions for each method that makescoding of the tracking filter simple and easy to understand. Case studies are employed to showcase applications of thediscussed topics. In addition, the book supplies block diagrams foreach algorithm, allowing readers to develop their own MATLAB®toolbox of estimation methods. Bayesian Estimation and Tracking is an excellent book forcourses on estimation and tracking methods at the graduate level.The book also serves as a valuable reference for researchscientists, mathematicians, and engineers seeking a deeperunderstanding of the topics.

Bayesian Estimation and Tracking

Author: Anton J. Haug
Publisher: John Wiley & Sons
ISBN: 1118287835
Format: PDF, Docs
Download Now
A practical approach to estimating and tracking dynamicsystems in real-worl applications Much of the literature on performing estimation for non-Gaussiansystems is short on practical methodology, while Gaussian methodsoften lack a cohesive derivation. Bayesian Estimation andTracking addresses the gap in the field on both accounts,providing readers with a comprehensive overview of methods forestimating both linear and nonlinear dynamic systems driven byGaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation andtracking, the book emphasizes the derivation of all trackingalgorithms within a Bayesian framework and describes effectivenumerical methods for evaluating density-weighted integrals,including linear and nonlinear Kalman filters for Gaussian-weightedintegrals and particle filters for non-Gaussian cases. The authorfirst emphasizes detailed derivations from first principles ofeeach estimation method and goes on to use illustrative anddetailed step-by-step instructions for each method that makescoding of the tracking filter simple and easy to understand. Case studies are employed to showcase applications of thediscussed topics. In addition, the book supplies block diagrams foreach algorithm, allowing readers to develop their own MATLAB®toolbox of estimation methods. Bayesian Estimation and Tracking is an excellent book forcourses on estimation and tracking methods at the graduate level.The book also serves as a valuable reference for researchscientists, mathematicians, and engineers seeking a deeperunderstanding of the topics.

Non Cooperative Target Tracking Fusion and Control

Author: Zhongliang Jing
Publisher: Springer
ISBN: 3319907166
Format: PDF, ePub, Docs
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This book gives a concise and comprehensive overview of non-cooperative target tracking, fusion and control. Focusing on algorithms rather than theories for non-cooperative targets including air and space-borne targets, this work explores a number of advanced techniques, including Gaussian mixture cardinalized probability hypothesis density (CPHD) filter, optimization on manifold, construction of filter banks and tight frames, structured sparse representation, and others. Containing a variety of illustrative and computational examples, Non-cooperative Target Tracking, Fusion and Control will be useful for students as well as engineers with an interest in information fusion, aerospace applications, radar data processing and remote sensing.

Nonlinear Time Series Analysis

Author: Ruey S. Tsay
Publisher: John Wiley & Sons
ISBN: 1119264065
Format: PDF, Kindle
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A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authors—noted experts in the field—explore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: • Offers research developed by leading scholars of time series analysis • Presents R commands making it possible to reproduce all the analyses included in the text • Contains real-world examples throughout the book • Recommends exercises to test understanding of material presented • Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models.

Adaptive Mobile Computing

Author: Mauro Migliardi
Publisher: Academic Press
ISBN: 0128046104
Format: PDF, ePub, Docs
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Adaptive Mobile Computing: Advances in Processing Mobile Data Sets explores the latest advancements in producing, processing and securing mobile data sets. The book provides the elements needed to deepen understanding of this trend which, over the last decade, has seen exponential growth in the number and capabilities of mobile devices. The pervasiveness, sensing capabilities and computational power of mobile devices have turned them into a fundamental instrument in everyday life for a large part of the human population. This fact makes mobile devices an incredibly rich source of data about the dynamics of human behavior, a pervasive wireless sensors network with substantial computational power and an extremely appealing target for a new generation of threats. Offers a coherent and realistic image of today’s architectures, techniques, protocols, components, orchestration, choreography and development related to mobile computing Explains state-of-the-art technological solutions for the main issues hindering the development of next-generation pervasive systems including: supporting components for collecting data intelligently, handling resource and data management, accounting for fault tolerance, security, monitoring and control, addressing the relation with the Internet of Things and Big Data and depicting applications for pervasive context-aware processing Presents the benefits of mobile computing and the development process of scientific and commercial applications and platforms to support them Familiarizes readers with the concepts and technologies that are successfully used in the implementation of pervasive/ubiquitous systems

Die Berechnung der Zukunft

Author: Nate Silver
Publisher: Heyne Verlag
ISBN: 3641112702
Format: PDF, ePub, Mobi
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Zuverlässige Vorhersagen sind doch möglich! Nate Silver ist der heimliche Gewinner der amerikanischen Präsidentschaftswahlen 2012: ein begnadeter Statistiker, als »Prognose-Popstar« und »Wundernerd« weltberühmt geworden. Er hat die Wahlergebnisse aller 50 amerikanischen Bundesstaaten absolut exakt vorausgesagt – doch damit nicht genug: Jetzt zeigt Nate Silver, wie seine Prognosen in Zukunft Terroranschläge, Umweltkatastrophen und Finanzkrisen verhindern sollen. Gelingt ihm die Abschaffung des Zufalls? Warum werden Wettervorhersagen immer besser, während die Terrorattacken vom 11.09.2001 niemand kommen sah? Warum erkennen Ökonomen eine globale Finanzkrise nicht einmal dann, wenn diese bereits begonnen hat? Das Problem ist nicht der Mangel an Informationen, sondern dass wir die verfügbaren Daten nicht richtig deuten. Zuverlässige Prognosen aber würden uns helfen, Zufälle und Ungewissheiten abzuwehren und unser Schicksal selbst zu bestimmen. Nate Silver zeigt, dass und wie das geht. Erstmals wendet er seine Wahrscheinlichkeitsrechnung nicht nur auf Wahlprognosen an, sondern auf die großen Probleme unserer Zeit: die Finanzmärkte, Ratingagenturen, Epidemien, Erdbeben, den Klimawandel, den Terrorismus. In all diesen Fällen gibt es zahlreiche Prognosen von Experten, die er überprüft – und erklärt, warum sie meist falsch sind. Gleichzeitig schildert er, wie es gelingen kann, im Rauschen der Daten die wesentlichen Informationen herauszufiltern. Ein unterhaltsamer und spannender Augenöffner!

Kalman Bucy Filter

Author: Karl Brammer
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3486785524
Format: PDF, ePub
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Das Buch führt den Leser auf elementarem Wege in die Wahrscheinlichkeitsrechnung und in die Theorie der Zufallsprozesse ein, wobei keinerlei Vorkenntnisse auf diesem Gebiet vorausgesetzt werden. Schließlich wird gezeigt, wie sich die Eigenschaften eines Zufallsprozesses bei der Übertragung durch ein lineares System verändern und wie diese veränderten Eigenschaften berechnet werden können.

Bayesian Multiple Target Tracking Second Edition

Author: Lawrence D. Stone
Publisher: Artech House
ISBN: 1608075532
Format: PDF, ePub, Mobi
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This second edition has undergone substantial revision from the 1999 first edition, recognizing that a lot has changed in the multiple target tracking field. One of the most dramatic changes is in the widespread use of particle filters to implement nonlinear, non-Gaussian Bayesian trackers. This book views multiple target tracking as a Bayesian inference problem. Within this framework it develops the theory of single target tracking, multiple target tracking, and likelihood ratio detection and tracking. In addition to providing a detailed description of a basic particle filter that implements the Bayesian single target recursion, this resource provides numerous examples that involve the use of particle filters. With these examples illustrating the developed concepts, algorithms, and approaches -- the book helps radar engineers develop tracking solutions when observations are non-linear functions of target state, when the target state distributions or measurement error distributions are not Gaussian, in low data rate and low signal to noise ratio situations, and when notions of contact and association are merged or unresolved among more than one target.

Kalman Filter F r Einsteiger

Author: Phil Kim
Publisher: Createspace Independent Publishing Platform
ISBN: 9781502723789
Format: PDF, ePub, Mobi
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Das Kalman-Filter ist eine Wunderwaffe, wenn es darum geht digitale Signale in Echtzeit vom Rauschen zu befreien,nicht messbare Signale zu sch�tzen, Objekte zu tracken, Daten zu fusionieren und Messaussetzer zu �berbr�cken.Es findet Anwendung in Robotern, Raketen, Automobilen und selbst in M�hrobotern und autonomen Staubsaugern. H�ufig sind Ingenieurinnen und Ingenieure jedoch von der umfassenden Mathematik abgeschreckt. Das Buch "Kalman-Filter f�r Einsteiger" w�hlt einen unkonventionellen Einstieg in diese aktuelle Filtertechnik.Es beschreibt pragmatisch anhand von zahlreichen MATLAB(R)-Beispielen die Grundlagen des Filters und lehrt die Auslegung ohnemathematische Herleitungen. Dieses Buch erleichtert den Einstieg in die Signalverarbeitung und macht Sie in kurzer Zeit zu Kalman-Filter-Anwendungsprofis.