State Estimation Using Measurements with Uncertain Time-tag PDF Download
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Author: Ariel Rubanenko
Publisher:
ISBN:
Category :
Languages : en
Pages : 156
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Book Description
Author: Ariel Rubanenko
Publisher:
ISBN:
Category :
Languages : en
Pages : 156
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Book Description
Author: Timothy D. Barfoot
Publisher: Cambridge University Press
ISBN: 1107159393
Category : Computers
Languages : en
Pages : 381
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Book Description
A modern look at state estimation, targeted at students and practitioners of robotics, with emphasis on three-dimensional applications.
Author: Ronald Loy Mitchell
Publisher:
ISBN:
Category :
Languages : en
Pages : 232
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Book Description
Author: Bangjun Lei
Publisher: John Wiley & Sons
ISBN: 1119152445
Category : Science
Languages : en
Pages : 483
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Book Description
A practical introduction to intelligent computer vision theory, design, implementation, and technology The past decade has witnessed epic growth in image processing and intelligent computer vision technology. Advancements in machine learning methods—especially among adaboost varieties and particle filtering methods—have made machine learning in intelligent computer vision more accurate and reliable than ever before. The need for expert coverage of the state of the art in this burgeoning field has never been greater, and this book satisfies that need. Fully updated and extensively revised, this 2nd Edition of the popular guide provides designers, data analysts, researchers and advanced post-graduates with a fundamental yet wholly practical introduction to intelligent computer vision. The authors walk you through the basics of computer vision, past and present, and they explore the more subtle intricacies of intelligent computer vision, with an emphasis on intelligent measurement systems. Using many timely, real-world examples, they explain and vividly demonstrate the latest developments in image and video processing techniques and technologies for machine learning in computer vision systems, including: PRTools5 software for MATLAB—especially the latest representation and generalization software toolbox for PRTools5 Machine learning applications for computer vision, with detailed discussions of contemporary state estimation techniques vs older content of particle filter methods The latest techniques for classification and supervised learning, with an emphasis on Neural Network, Genetic State Estimation and other particle filter and AI state estimation methods All new coverage of the Adaboost and its implementation in PRTools5. A valuable working resource for professionals and an excellent introduction for advanced-level students, this 2nd Edition features a wealth of illustrative examples, ranging from basic techniques to advanced intelligent computer vision system implementations. Additional examples and tutorials, as well as a question and solution forum, can be found on a companion website.
Author: Matthew Simon Dyer
Publisher:
ISBN:
Category :
Languages : en
Pages : 285
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Book Description
Author: Yang Liu
Publisher: CRC Press
ISBN: 1000641066
Category : Technology & Engineering
Languages : en
Pages : 223
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Book Description
The objective of this book is to present the up-to-date research developments and novel methodologies on state estimation and fault diagnosis (FD) techniques for a class of complex systems subject to closed-loop control, nonlinearities, and stochastic phenomena. It covers state estimation design methodologies and FD unit design methodologies including framework of optimal filter and FD unit design, robust filter and FD unit design, stability, and performance analysis for the considered systems subject to various kinds of complex factors. Features: Reviews latest research results on the state estimation and fault diagnosis issues. Presents comprehensive framework constituted for systems under imperfect measurements. Includes quantitative performance analyses to solve problems in practical situations. Provides simulation examples extracted from practical engineering scenarios. Discusses proper and novel techniques such as the Carleman approximation and completing the square method is employed to solve the mathematical problems. This book aims at Graduate students, Professionals and Researchers in Control Science and Application, Stochastic Process, Fault Diagnosis, and Instrumentation and Measurement.
Author:
Publisher:
ISBN:
Category : Artificial satellites
Languages : en
Pages : 606
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Book Description
Author: Yuriy S. Shmaliy
Publisher: John Wiley & Sons
ISBN: 1119863090
Category : Technology & Engineering
Languages : en
Pages : 484
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Book Description
A unified and systematic theoretical framework for solving problems related to finite impulse response (FIR) estimate Optimal and Robust State Estimation: Finite Impulse Response (FIR) and Kalman Approaches is a comprehensive investigation into batch state estimators and recursive forms. The work begins by introducing the reader to the state estimation approach and provides a brief historical overview. Next, the work discusses the specific properties of finite impulse response (FIR) state estimators. Further chapters give the basics of probability and stochastic processes, discuss the available linear and nonlinear state estimators, deal with optimal FIR filtering, and consider a limited memory batch and recursive algorithms. Other topics covered include solving the q-lag FIR smoothing problem, introducing the receding horizon (RH) FIR state estimation approach, and developing the theory of FIR state estimation under disturbances. The book closes by discussing the theory of FIR state estimation for uncertain systems and providing several applications where the FIR state estimators are used effectively. Key concepts covered in the work include: A holistic overview of the state estimation approach, which arose from the need to know the internal state of a real system, given that the input and output are both known Optimal, optimal unbiased, maximum likelihood, and unbiased and robust finite impulse response (FIR) structures FIR state estimation approach along with the infinite impulse response (IIR) and Kalman approaches Cost functions and the most critical properties of FIR and IIR state estimates Optimal and Robust State Estimation: Finite Impulse Response (FIR) and Kalman Approaches was written for professionals in the fields of microwave engineering, system engineering, and robotics who wish to move towards solving finite impulse response (FIR) estimate issues in both theoretical and practical applications. Graduate and senior undergraduate students with coursework dealing with state estimation will also be able to use the book to gain a valuable foundation of knowledge and become more adept in their chosen fields of study.
Author: Simo Särkkä
Publisher: Cambridge University Press
ISBN: 110703065X
Category : Computers
Languages : en
Pages : 255
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Book Description
A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Author: Chun-Chia Huang
Publisher:
ISBN:
Category :
Languages : en
Pages : 90
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Book Description
The problem of state estimation for a linear, time-varying, Gaussian system from measurements which are communicated over an imperfect channel is considered from several perspectives. The communication imperfections include intermittency, channel noise, quantization, etc. The first part of the thesis examines the stochastic behavior of the state estimation error and of the regulated state itself in situations of intermittent and quantized measurements via the formulation of an escape time problem dealing with the cumulative distribution function of the probability of escape of these signals from a given set. This is compared to and contrasted with earlier analyses which considered the behavior of Kalman filters with intermittent data based on moments and conditional moments, and the evaluation of the minimal number of bits required for mean square stabilization. The main result shows the escape time is characterized by a Markov chain which is amenable to explicit analysis through the calculation of the its cumulative distribution function. The second part of the thesis focuses on developing an exact formulation of the conditional probability density function of the system state given quantized innovations signals communicated from a linear Kalman filter at the transmitter. This is based on Bayesian filtering and extends previous works on the subject but without the requirement for simplifying assumptions. This latter result follows from a simple observation concerning the correct choice of state for the transmitter, which includes the transmitters' Kalman filter estimate. This leads to an exact and recursive approach.
