Kalman Filtering

Kalman Filtering
Author: Mohinder S. Grewal,Angus P. Andrews
Publsiher: John Wiley & Sons
Total Pages: 640
Release: 2015-02-02
ISBN 10: 111898496X
ISBN 13: 9781118984963
Language: EN, FR, DE, ES & NL

Kalman Filtering Book Review:

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.

Kalman Filters

Kalman Filters
Author: Ginalber Luiz Serra
Publsiher: BoD – Books on Demand
Total Pages: 314
Release: 2018-02-21
ISBN 10: 9535138278
ISBN 13: 9789535138273
Language: EN, FR, DE, ES & NL

Kalman Filters Book Review:

This book presents recent issues on theory and practice of Kalman filters, with a comprehensive treatment of a selected number of concepts, techniques, and advanced applications. From an interdisciplinary point of view, the contents from each chapter bring together an international scientific community to discuss the state of the art on Kalman filter-based methodologies for adaptive/distributed filtering, optimal estimation, dynamic prediction, nonstationarity, robot navigation, global navigation satellite systems, moving object tracking, optical communication systems, and active power filters, among others. The theoretical and methodological foundations combined with extensive experimental explanation make this book a reference suitable for students, practicing engineers, and researchers in sciences and engineering.

Kalman Filtering and Neural Networks

Kalman Filtering and Neural Networks
Author: Simon Haykin
Publsiher: John Wiley & Sons
Total Pages: 284
Release: 2004-03-24
ISBN 10: 047146421X
ISBN 13: 9780471464211
Language: EN, FR, DE, ES & NL

Kalman Filtering and Neural Networks Book Review:

State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover: An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF) Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm The unscented Kalman filter Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.

Beyond the Kalman Filter Particle Filters for Tracking Applications

Beyond the Kalman Filter  Particle Filters for Tracking Applications
Author: Branko Ristic ,Sanjeev Arulampalam,Neil Gordon
Publsiher: Artech House
Total Pages: 299
Release: 2003-12-01
ISBN 10: 9781580538510
ISBN 13: 1580538517
Language: EN, FR, DE, ES & NL

Beyond the Kalman Filter Particle Filters for Tracking Applications Book Review:

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.

Tracking and Kalman Filtering Made Easy

Tracking and Kalman Filtering Made Easy
Author: Eli Brookner
Publsiher: Wiley-Interscience
Total Pages: 477
Release: 1998
ISBN 10: 1928374650XXX
ISBN 13: UOM:39015040375092
Language: EN, FR, DE, ES & NL

Tracking and Kalman Filtering Made Easy Book Review:

This book is about radar tracking and the use of filters, particularly Kalman Filters. Tracking of moving targets, such as satellites, is complicated by the introduction of errors into the measurements resulting from noise and non-uniform vehicle motion. Such errors are smoothed out by filters.

A Kalman Filter Primer

A Kalman Filter Primer
Author: Randall L. Eubank
Publsiher: CRC Press
Total Pages: 200
Release: 2005-11-29
ISBN 10: 9781420028676
ISBN 13: 1420028677
Language: EN, FR, DE, ES & NL

A Kalman Filter Primer Book Review:

System state estimation in the presence of noise is critical for control systems, signal processing, and many other applications in a variety of fields. Developed decades ago, the Kalman filter remains an important, powerful tool for estimating the variables in a system in the presence of noise. However, when inundated with theory and vast notations, learning just how the Kalman filter works can be a daunting task. With its mathematically rigorous, “no frills” approach to the basic discrete-time Kalman filter, A Kalman Filter Primer builds a thorough understanding of the inner workings and basic concepts of Kalman filter recursions from first principles. Instead of the typical Bayesian perspective, the author develops the topic via least-squares and classical matrix methods using the Cholesky decomposition to distill the essence of the Kalman filter and reveal the motivations behind the choice of the initializing state vector. He supplies pseudo-code algorithms for the various recursions, enabling code development to implement the filter in practice. The book thoroughly studies the development of modern smoothing algorithms and methods for determining initial states, along with a comprehensive development of the “diffuse” Kalman filter. Using a tiered presentation that builds on simple discussions to more complex and thorough treatments, A Kalman Filter Primer is the perfect introduction to quickly and effectively using the Kalman filter in practice.

Kalman Filter for Beginners

Kalman Filter for Beginners
Author: Phil Kim,Lynn Huh
Publsiher: CreateSpace
Total Pages: 231
Release: 2011
ISBN 10: 9781463648350
ISBN 13: 1463648359
Language: EN, FR, DE, ES & NL

Kalman Filter for Beginners Book Review:

Dwarfs your fear towards complicated mathematical derivations and proofs. Experience Kalman filter with hands-on examples to grasp the essence. A book long awaited by anyone who could not dare to put their first step into Kalman filter. The author presents Kalman filter and other useful filters without complicated mathematical derivation and proof but with hands-on examples in MATLAB that will guide you step-by-step. The book starts with recursive filter and basics of Kalman filter, and gradually expands to application for nonlinear systems through extended and unscented Kalman filters. Also, some topics on frequency analysis including complementary filter are covered. Each chapter is balanced with theoretical background for absolute beginners and practical MATLAB examples to experience the principles explained. Once grabbing the book, you will notice it is not fearful but even enjoyable to learn Kalman filter.

Kalman Filter

Kalman Filter
Author: Víctor M. Moreno,Alberto Pigazo
Publsiher: BoD – Books on Demand
Total Pages: 606
Release: 2009-04-01
ISBN 10: 9533070005
ISBN 13: 9789533070001
Language: EN, FR, DE, ES & NL

Kalman Filter Book Review:

The aim of this book is to provide an overview of recent developments in Kalman filter theory and their applications in engineering and scientific fields. The book is divided into 24 chapters and organized in five blocks corresponding to recent advances in Kalman filtering theory, applications in medical and biological sciences, tracking and positioning systems, electrical engineering and, finally, industrial processes and communication networks.

Advanced Kalman Filtering Least Squares and Modeling

Advanced Kalman Filtering  Least Squares and Modeling
Author: Bruce P. Gibbs
Publsiher: John Wiley & Sons
Total Pages: 640
Release: 2011-03-29
ISBN 10: 1118003160
ISBN 13: 9781118003169
Language: EN, FR, DE, ES & NL

Advanced Kalman Filtering Least Squares and Modeling Book Review:

This book is intended primarily as a handbook for engineers who must design practical systems. Its primary goal is to discuss model development in sufficient detail so that the reader may design an estimator that meets all application requirements and is robust to modeling assumptions. Since it is sometimes difficult to a priori determine the best model structure, use of exploratory data analysis to define model structure is discussed. Methods for deciding on the “best” model are also presented. A second goal is to present little known extensions of least squares estimation or Kalman filtering that provide guidance on model structure and parameters, or make the estimator more robust to changes in real-world behavior. A third goal is discussion of implementation issues that make the estimator more accurate or efficient, or that make it flexible so that model alternatives can be easily compared. The fourth goal is to provide the designer/analyst with guidance in evaluating estimator performance and in determining/correcting problems. The final goal is to provide a subroutine library that simplifies implementation, and flexible general purpose high-level drivers that allow both easy analysis of alternative models and access to extensions of the basic filtering. Supplemental materials and up-to-date errata are downloadable at http://booksupport.wiley.com.

Robust Kalman Filtering for Signals and Systems with Large Uncertainties

Robust Kalman Filtering for Signals and Systems with Large Uncertainties
Author: Ian R. Petersen,Andrey V. Savkin
Publsiher: Springer Science & Business Media
Total Pages: 207
Release: 2012-12-06
ISBN 10: 1461215943
ISBN 13: 9781461215943
Language: EN, FR, DE, ES & NL

Robust Kalman Filtering for Signals and Systems with Large Uncertainties Book Review:

A significant shortcoming of the state space control theory that emerged in the 1960s was its lack of concern for the issue of robustness. However, in the design of feedback control systems, robustness is a critical issue. These facts led to great activity in the research area of robust control theory. One of the major developments of modern control theory was the Kalman Filter and hence the development of a robust version of the Kalman Filter has become an active area of research. Although the issue of robustness in filtering is not as critical as in feedback control (where there is always the issue of instability to worry about), research on robust filtering and state estimation has remained very active in recent years. However, although numerous books have appeared on the topic of Kalman filtering, this book is one of the first to appear on robust Kalman filtering. Most of the material presented in this book derives from a period of research collaboration between the authors from 1992 to 1994. However, its origins go back earlier than that. The first author (LR. P. ) became in terested in problems of robust filtering through his research collaboration with Dr. Duncan McFarlane. At this time, Dr. McFarlane was employed at the Melbourne Research Laboratories ofBHP Ltd. , a large Australian min erals, resources, and steel processing company.

The Kalman Filter in Finance

The Kalman Filter in Finance
Author: C. Wells
Publsiher: Springer Science & Business Media
Total Pages: 172
Release: 2013-03-09
ISBN 10: 940158611X
ISBN 13: 9789401586115
Language: EN, FR, DE, ES & NL

The Kalman Filter in Finance Book Review:

A non-technical introduction to the question of modeling with time-varying parameters, using the beta coefficient from Financial Economics as the main example. After a brief introduction to this coefficient for those not versed in finance, the book presents a number of rather well known tests for constant coefficients and then performs these tests on data from the Stockholm Exchange. The Kalman filter is then introduced and a simple example is used to demonstrate the power of the filter. The filter is then used to estimate the market model with time-varying betas. The book concludes with further examples of how the Kalman filter may be used in estimation models used in analyzing other aspects of finance. Since both the programs and the data used in the book are available for downloading, the book is especially valuable for students and other researchers interested in learning the art of modeling with time varying coefficients.

Optimal State Estimation

Optimal State Estimation
Author: Dan Simon
Publsiher: John Wiley & Sons
Total Pages: 552
Release: 2006-06-19
ISBN 10: 0470045337
ISBN 13: 9780470045336
Language: EN, FR, DE, ES & NL

Optimal State Estimation Book Review:

A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.

Progress in Astronautics and Aeronautics

Progress in Astronautics and Aeronautics
Author: Anonim
Publsiher: Unknown
Total Pages: 135
Release: 1963
ISBN 10: 9781600867187
ISBN 13: 1600867189
Language: EN, FR, DE, ES & NL

Progress in Astronautics and Aeronautics Book Review:

Kalman Filtering Techniques for Radar Tracking

Kalman Filtering Techniques for Radar Tracking
Author: K.V. Ramachandra
Publsiher: CRC Press
Total Pages: 256
Release: 2018-03-12
ISBN 10: 148227311X
ISBN 13: 9781482273113
Language: EN, FR, DE, ES & NL

Kalman Filtering Techniques for Radar Tracking Book Review:

A review of effective radar tracking filter methods and their associated digital filtering algorithms. It examines newly developed systems for eliminating the real-time execution of complete recursive Kalman filtering matrix equations that reduce tracking and update time. It also focuses on the role of tracking filters in operations of radar data processors for satellites, missiles, aircraft, ships, submarines and RPVs.

Introduction and Implementations of the Kalman Filter

Introduction and Implementations of the Kalman Filter
Author: Felix Govaers
Publsiher: BoD – Books on Demand
Total Pages: 128
Release: 2019-05-22
ISBN 10: 1838805362
ISBN 13: 9781838805364
Language: EN, FR, DE, ES & NL

Introduction and Implementations of the Kalman Filter Book Review:

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.

Restricted Kalman Filtering

Restricted Kalman Filtering
Author: Adrian Pizzinga
Publsiher: Springer Science & Business Media
Total Pages: 62
Release: 2012-07-25
ISBN 10: 1461447380
ISBN 13: 9781461447382
Language: EN, FR, DE, ES & NL

Restricted Kalman Filtering Book Review:

​​​​​​​​ ​In statistics, the Kalman filter is a mathematical method whose purpose is to use a series of measurements observed over time, containing random variations and other inaccuracies, and produce estimates that tend to be closer to the true unknown values than those that would be based on a single measurement alone. This Brief offers developments on Kalman filtering subject to general linear constraints. There are essentially three types of contributions: new proofs for results already established; new results within the subject; and applications in investment analysis and macroeconomics, where the proposed methods are illustrated and evaluated. The Brief has a short chapter on linear state space models and the Kalman filter, aiming to make the book self-contained and to give a quick reference to the reader (notation and terminology). The prerequisites would be a contact with time series analysis in the level of Hamilton (1994) or Brockwell & Davis (2002) and also with linear state models and the Kalman filter – each of these books has a chapter entirely dedicated to the subject. The book is intended for graduate students, researchers and practitioners in statistics (specifically: time series analysis and econometrics).

Estimation Control and the Discrete Kalman Filter

Estimation  Control  and the Discrete Kalman Filter
Author: Donald E. Catlin
Publsiher: Springer Science & Business Media
Total Pages: 276
Release: 2012-12-06
ISBN 10: 1461245281
ISBN 13: 9781461245285
Language: EN, FR, DE, ES & NL

Estimation Control and the Discrete Kalman Filter Book Review:

In 1960, R. E. Kalman published his celebrated paper on recursive min imum variance estimation in dynamical systems [14]. This paper, which introduced an algorithm that has since been known as the discrete Kalman filter, produced a virtual revolution in the field of systems engineering. Today, Kalman filters are used in such diverse areas as navigation, guid ance, oil drilling, water and air quality, and geodetic surveys. In addition, Kalman's work led to a multitude of books and papers on minimum vari ance estimation in dynamical systems, including one by Kalman and Bucy on continuous time systems [15]. Most of this work was done outside of the mathematics and statistics communities and, in the spirit of true academic parochialism, was, with a few notable exceptions, ignored by them. This text is my effort toward closing that chasm. For mathematics students, the Kalman filtering theorem is a beautiful illustration of functional analysis in action; Hilbert spaces being used to solve an extremely important problem in applied mathematics. For statistics students, the Kalman filter is a vivid example of Bayesian statistics in action. The present text grew out of a series of graduate courses given by me in the past decade. Most of these courses were given at the University of Mas sachusetts at Amherst.

Intuitive Understanding of Kalman Filtering with MATLAB

Intuitive Understanding of Kalman Filtering with MATLAB
Author: Armando Barreto,Malek Adjouadi,Francisco R. Ortega,Nonnarit O-larnnithipong
Publsiher: CRC Press
Total Pages: 230
Release: 2020-09-06
ISBN 10: 0429577567
ISBN 13: 9780429577567
Language: EN, FR, DE, ES & NL

Intuitive Understanding of Kalman Filtering with MATLAB Book Review:

The emergence of affordable micro sensors, such as MEMS Inertial Measurement Systems, are applied in embedded systems and Internet-of-Things devices. This has brought techniques such as Kalman Filtering, which are capable of combining information from multiple sensors or sources, to the interest of students and hobbyists. This book will explore the necessary background concepts, helping a much wider audience of readers develop an understanding and intuition that will enable them to follow the explanation for the Kalman Filtering algorithm. Key Features: Provides intuitive understanding of Kalman Filtering approach Succinct overview of concepts to enhance accessibility and appeal to a wide audience Interactive learning techniques with code examples Malek Adjouadi, PhD, is Ware Professor with the Department of Electrical and Computer Engineering at Florida International University, Miami. He received his PhD from the Electrical Engineering Department at the University of Florida, Gainesville. He is the Founding Director of the Center for Advanced Technology and Education funded by the National Science Foundation. His earlier work on computer vision to help persons with blindness led to his testimony to the U.S. Senate on the committee of Veterans Affairs on the subject of technology to help persons with disabilities. His research interests are in imaging, signal processing and machine learning, with applications in brain research and assistive technology. Armando Barreto, PhD, is Professor of the Electrical and Computer Engineering Department at Florida International University, Miami, as well as the Director of FIU’s Digital Signal Processing Laboratory, with more than 25 years of experience teaching DSP to undergraduate and graduate students. He earned his PhD in electrical engineering from the University of Florida, Gainesville. His work has focused on applying DSP techniques to the facilitation of human-computer interactions, particularly for the benefit of individuals with disabilities. He has developed human-computer interfaces based on the processing of signals and has developed a system that adds spatialized sounds to the icons in a computer interface to facilitate access by individuals with "low vision." With his research team, he has explored the use of Magnetic, Angular-Rate and Gravity (MARG) sensor modules and Inertial Measurement Units (IMUs) for human-computer interaction applications. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM). Francisco R. Ortega, PhD, is an Assistant Professor at Colorado State University and Director of the Natural User Interaction Lab (NUILAB). Dr. Ortega earned his PhD in Computer Science (CS) in the field of Human-Computer Interaction (HCI) and 3D User Interfaces (3DUI) from Florida International University (FIU). He also held a position of Post-Doc and Visiting Assistant Professor at FIU. His main research area focuses on improving user interaction in 3DUI by (a) eliciting (hand and full-body) gesture and multimodal interactions, (b) developing techniques for multimodal interaction, and (c) developing interactive multimodal recognition systems. His secondary research aims to discover how to increase interest for CS in non-CS entry-level college students via virtual and augmented reality games. His research has resulted in multiple peer-reviewed publications in venues such as ACM ISS, ACM SUI, and IEEE 3DUI, among others. He is the first-author of the CRC Press book Interaction Design for 3D User Interfaces: The World of Modern Input Devices for Research, Applications and Game Development. Nonnarit O-larnnithipong, PhD, is an Instructor at Florida International University. Dr. O-larnnithipong earned his PhD in Electrical Engineering, majoring in Digital Signal Processing from Florida International University (FIU). He also held a position of Post-Doctoral Associate at FIU in 2019. His research has focused on (1) implementing the sensor fusion algorithm to improve orientation measurement using MEMS inertial and magnetic sensors and (2) developing a 3D hand motion tracking system using Inertial Measurement Units (IMUs) and infrared cameras. His research has resulted in multiple peer-reviewed publications in venues such as HCI-International and IEEE Sensors.

Beyond the Kalman Filter

Beyond the Kalman Filter
Author: Branko Ristic,Sanjeev Arulampalam
Publsiher: Artech House Publishers
Total Pages: 299
Release: 2004-01
ISBN 10: 9781580536318
ISBN 13: 158053631X
Language: EN, FR, DE, ES & NL

Beyond the Kalman Filter Book Review:

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.

Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises and Solutions

Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises and Solutions
Author: Robert Grover Brown,Patrick Y. C. Hwang
Publsiher: John Wiley & Sons Incorporated
Total Pages: 484
Release: 1997
ISBN 10: 1928374650XXX
ISBN 13: UOM:39015040683321
Language: EN, FR, DE, ES & NL

Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises and Solutions Book Review:

In this updated edition the main thrust is on applied Kalman filtering. Chapters 1-3 provide a minimal background in random process theory and the response of linear systems to random inputs. The following chapter is devoted to Wiener filtering and the remainder of the text deals with various facets of Kalman filtering with emphasis on applications. Starred problems at the end of each chapter are computer exercises. The authors believe that programming the equations and analyzing the results of specific examples is the best way to obtain the insight that is essential in engineering work.