Invariant Kalman Filtering

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Introduction and Implementations of the Kalman Filter

Author : Felix Govaers
Publisher : BoD – Books on Demand
ISBN 13 : 1838805362
Total Pages : 130 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Introduction and Implementations of the Kalman Filter by : Felix Govaers

Download or read book Introduction and Implementations of the Kalman Filter written by Felix Govaers and published by BoD – Books on Demand. This book was released on 2019-05-22 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Optimal Filtering

Author : Brian D. O. Anderson
Publisher : Courier Corporation
ISBN 13 : 0486136892
Total Pages : 370 pages
Book Rating : 4.4/5 (861 download)

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Book Synopsis Optimal Filtering by : Brian D. O. Anderson

Download or read book Optimal Filtering written by Brian D. O. Anderson and published by Courier Corporation. This book was released on 2012-05-23 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graduate-level text extends studies of signal processing, particularly regarding communication systems and digital filtering theory. Topics include filtering, linear systems, and estimation; discrete-time Kalman filter; time-invariant filters; more. 1979 edition.

2018 21st International Conference on Information Fusion (FUSION)

Author : IEEE Staff
Publisher :
ISBN 13 : 9781538643303
Total Pages : pages
Book Rating : 4.6/5 (433 download)

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Book Synopsis 2018 21st International Conference on Information Fusion (FUSION) by : IEEE Staff

Download or read book 2018 21st International Conference on Information Fusion (FUSION) written by IEEE Staff and published by . This book was released on 2018-07-10 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Scope of the conference is to provide medium to discuss advances and applications of fusion methodologies Conference will include contributions in the areas of fusion methodologies, theory and representation, algorithms and modelling and simulation

Deep Neural Networks in a Mathematical Framework

Author : Anthony L. Caterini
Publisher : Springer
ISBN 13 : 3319753045
Total Pages : 95 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Deep Neural Networks in a Mathematical Framework by : Anthony L. Caterini

Download or read book Deep Neural Networks in a Mathematical Framework written by Anthony L. Caterini and published by Springer. This book was released on 2018-03-22 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks. This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but also to those outside of the neutral network community.

State Estimation for Robotics

Author : Timothy D. Barfoot
Publisher : Cambridge University Press
ISBN 13 : 1107159393
Total Pages : 381 pages
Book Rating : 4.1/5 (71 download)

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Book Synopsis State Estimation for Robotics by : Timothy D. Barfoot

Download or read book State Estimation for Robotics written by Timothy D. Barfoot and published by Cambridge University Press. This book was released on 2017-07-31 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: A modern look at state estimation, targeted at students and practitioners of robotics, with emphasis on three-dimensional applications.

Directions in Mathematical Systems Theory and Optimization

Author : Anders Rantzer
Publisher : Springer Science & Business Media
ISBN 13 : 3540000658
Total Pages : 392 pages
Book Rating : 4.5/5 (4 download)

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Book Synopsis Directions in Mathematical Systems Theory and Optimization by : Anders Rantzer

Download or read book Directions in Mathematical Systems Theory and Optimization written by Anders Rantzer and published by Springer Science & Business Media. This book was released on 2002-11-05 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: For more than three decades, Anders Lindquist has delivered fundamental cont- butions to the ?elds of systems, signals and control. Throughout this period, four themes can perhaps characterize his interests: Modeling, estimation and ?ltering, feedback and robust control. His contributions to modeling include seminal work on the role of splitting subspaces in stochastic realization theory, on the partial realization problem for both deterministic and stochastic systems, on the solution of the rational covariance extension problem and on system identi?cation. His contributions to ?ltering and estimation include the development of fast ?ltering algorithms, leading to a nonlinear dynamical system which computes spectral factors in its steady state, and which provide an alternate, linear in the dimension of the state space, to computing the Kalman gain from a matrix Riccati equation. His further research on the phase portrait of this dynamical system gave a better understanding of when the Kalman ?lter will converge, answering an open question raised by Kalman. While still a student he established the separation principle for stochastic function differential equations, including some fundamental work on optimal control for stochastic systems with time lags. He continued his interest in feedback control by deriving optimal and robust control feedback laws for suppressing the effects of harmonic disturbances. Moreover, his recent work on a complete parameterization of all rational solutions to the Nevanlinna-Pick problem is providing a new approach to robust control design.

Kalman Filtering

Author : Mohinder S. Grewal
Publisher : John Wiley & Sons
ISBN 13 : 111898496X
Total Pages : 639 pages
Book Rating : 4.1/5 (189 download)

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Book Synopsis Kalman Filtering by : Mohinder S. Grewal

Download or read book Kalman Filtering written by Mohinder S. Grewal and published by John Wiley & Sons. This book was released on 2015-02-02 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Nonlinear Kalman Filter for Multi-Sensor Navigation of Unmanned Aerial Vehicles

Author : Jean-Philippe Condomines
Publisher : Elsevier
ISBN 13 : 0081027443
Total Pages : 256 pages
Book Rating : 4.0/5 (81 download)

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Book Synopsis Nonlinear Kalman Filter for Multi-Sensor Navigation of Unmanned Aerial Vehicles by : Jean-Philippe Condomines

Download or read book Nonlinear Kalman Filter for Multi-Sensor Navigation of Unmanned Aerial Vehicles written by Jean-Philippe Condomines and published by Elsevier. This book was released on 2018-11-14 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear Kalman Filter for Multi-Sensor Navigation of Unmanned Aerial Vehicles covers state estimation development approaches for Mini-UAV. The book focuses on Kalman filtering technics for UAV design, proposing a new design methodology and case study related to inertial navigation systems for drones. Both simulation and real experiment results are presented, thus showing new and promising perspectives. - Gives a state estimation development approach for mini-UAVs - Explains Kalman filtering techniques - Introduce a new design method for unmanned aerial vehicles - Introduce cases relating to the inertial navigation system of drones

Optimal and Robust Estimation

Author : Frank L. Lewis
Publisher : CRC Press
ISBN 13 : 1420008293
Total Pages : 546 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Optimal and Robust Estimation by : Frank L. Lewis

Download or read book Optimal and Robust Estimation written by Frank L. Lewis and published by CRC Press. This book was released on 2017-12-19 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: More than a decade ago, world-renowned control systems authority Frank L. Lewis introduced what would become a standard textbook on estimation, under the title Optimal Estimation, used in top universities throughout the world. The time has come for a new edition of this classic text, and Lewis enlisted the aid of two accomplished experts to bring the book completely up to date with the estimation methods driving today's high-performance systems. A Classic Revisited Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, Second Edition reflects new developments in estimation theory and design techniques. As the title suggests, the major feature of this edition is the inclusion of robust methods. Three new chapters cover the robust Kalman filter, H-infinity filtering, and H-infinity filtering of discrete-time systems. Modern Tools for Tomorrow's Engineers This text overflows with examples that highlight practical applications of the theory and concepts. Design algorithms appear conveniently in tables, allowing students quick reference, easy implementation into software, and intuitive comparisons for selecting the best algorithm for a given application. In addition, downloadable MATLAB® code allows students to gain hands-on experience with industry-standard software tools for a wide variety of applications. This cutting-edge and highly interactive text makes teaching, and learning, estimation methods easier and more modern than ever.

Tracking and Kalman Filtering Made Easy

Author : Eli Brookner
Publisher : Wiley-Interscience
ISBN 13 :
Total Pages : 512 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Tracking and Kalman Filtering Made Easy by : Eli Brookner

Download or read book Tracking and Kalman Filtering Made Easy written by Eli Brookner and published by Wiley-Interscience. This book was released on 1998 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRACKING, PREDICTION, AND SMOOTHING BASICS. g and g-h-k Filters. Kalman Filter. Practical Issues for Radar Tracking. LEAST-SQUARES FILTERING, VOLTAGE PROCESSING, ADAPTIVE ARRAY PROCESSING, AND EXTENDED KALMAN FILTER. Least-Squares and Minimum-Variance Estimates for Linear Time-Invariant Systems. Fixed-Memory Polynomial Filter. Expanding- Memory (Growing-Memory) Polynomial Filters. Fading-Memory (Discounted Least-Squares) Filter. General Form for Linear Time-Invariant System. General Recursive Minimum-Variance Growing-Memory Filter (Bayes and Kalman Filters without Target Process Noise). Voltage Least-Squares Algorithms Revisited. Givens Orthonormal Transformation. Householder Orthonormal Transformation. Gram--Schmidt Orthonormal Transformation. More on Voltage-Processing Techniques. Linear Time-Variant System. Nonlinear Observation Scheme and Dynamic Model (Extended Kalman Filter). Bayes Algorithm with Iterative Differential Correction for Nonlinear Systems. Kalman Filter Revisited. Appendix. Problems. Symbols and Acronyms. Solution to Selected Problems. References. Index.

China Satellite Navigation Conference (CSNC 2021) Proceedings

Author : Changfeng Yang
Publisher : Springer Nature
ISBN 13 : 9811631425
Total Pages : 703 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis China Satellite Navigation Conference (CSNC 2021) Proceedings by : Changfeng Yang

Download or read book China Satellite Navigation Conference (CSNC 2021) Proceedings written by Changfeng Yang and published by Springer Nature. This book was released on 2021-06-10 with total page 703 pages. Available in PDF, EPUB and Kindle. Book excerpt: China Satellite Navigation Conference (CSNC 2021) Proceedings presents selected research papers from CSNC 2021 held during 22nd-25th May, 2021 in Nanchang, China. These papers discuss the technologies and applications of the Global Navigation Satellite System (GNSS), and the latest progress made in the China BeiDou System (BDS) especially. They are divided into 10 topics to match the corresponding sessions in CSNC2021 which broadly covered key topics in GNSS. Readers can learn about the BDS and keep abreast of the latest advances in GNSS techniques and applications.

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.

Kalman Filtering and Information Fusion

Author : Hongbin Ma
Publisher : Springer Nature
ISBN 13 : 9811508062
Total Pages : 295 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Kalman Filtering and Information Fusion by : Hongbin Ma

Download or read book Kalman Filtering and Information Fusion written by Hongbin Ma and published by Springer Nature. This book was released on 2019-11-27 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses a key technology for digital information processing: Kalman filtering, which is generally considered to be one of the greatest discoveries of the 20th century. It introduces readers to issues concerning various uncertainties in a single plant, and to corresponding solutions based on adaptive estimation. Further, it discusses in detail the issues that arise when Kalman filtering technology is applied in multi-sensor systems and/or multi-agent systems, especially when various sensors are used in systems like intelligent robots, autonomous cars, smart homes, smart buildings, etc., requiring multi-sensor information fusion techniques. Furthermore, when multiple agents (subsystems) interact with one another, it produces coupling uncertainties, a challenging issue that is addressed here with the aid of novel decentralized adaptive filtering techniques.Overall, the book’s goal is to provide readers with a comprehensive investigation into the challenging problem of making Kalman filtering work well in the presence of various uncertainties and/or for multiple sensors/components. State-of-art techniques are introduced, together with a wealth of novel findings. As such, it can be a good reference book for researchers whose work involves filtering and applications; yet it can also serve as a postgraduate textbook for students in mathematics, engineering, automation, and related fields.To read this book, only a basic grasp of linear algebra and probability theory is needed, though experience with least squares, navigation, robotics, etc. would definitely be a plus.

Probabilistic Robotics

Author : Sebastian Thrun
Publisher : MIT Press
ISBN 13 : 0262201623
Total Pages : 668 pages
Book Rating : 4.2/5 (622 download)

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Book Synopsis Probabilistic Robotics by : Sebastian Thrun

Download or read book Probabilistic Robotics written by Sebastian Thrun and published by MIT Press. This book was released on 2005-08-19 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

Forecasting, Structural Time Series Models and the Kalman Filter

Author : Andrew C. Harvey
Publisher : Cambridge University Press
ISBN 13 : 9780521405737
Total Pages : 574 pages
Book Rating : 4.4/5 (57 download)

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Book Synopsis Forecasting, Structural Time Series Models and the Kalman Filter by : Andrew C. Harvey

Download or read book Forecasting, Structural Time Series Models and the Kalman Filter written by Andrew C. Harvey and published by Cambridge University Press. This book was released on 1990 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: A synthesis of concepts and materials, that ordinarily appear separately in time series and econometrics literature, presents a comprehensive review of theoretical and applied concepts in modeling economic and social time series.

Geometric Methods in System Theory

Author : D.Q. Mayne
Publisher : Springer Science & Business Media
ISBN 13 : 9401026750
Total Pages : 322 pages
Book Rating : 4.4/5 (1 download)

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Book Synopsis Geometric Methods in System Theory by : D.Q. Mayne

Download or read book Geometric Methods in System Theory written by D.Q. Mayne and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geometric Methods in System Theory In automatic control there are a large number of applications of a fairly simple type for which the motion of the state variables is not free to evolve in a vector space but rather must satisfy some constraints. Examples are numerous; in a switched, lossless electrical network energy is conserved and the state evolves on an ellipsoid surface defined by x'Qx equals a constant; in the control of finite state, continuous time, Markov processes the state evolves on the set x'x = 1, xi ~ O. The control of rigid body motions and trajectory control leads to problems of this type. There has been under way now for some time an effort to build up enough control theory to enable one to treat these problems in a more or less routine way. It is important to emphasise that the ordinary vector space-linear theory often gives the wrong insight and thus should not be relied upon.

Bayesian Inference of State Space Models

Author : Kostas Triantafyllopoulos
Publisher : Springer Nature
ISBN 13 : 303076124X
Total Pages : 503 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Bayesian Inference of State Space Models by : Kostas Triantafyllopoulos

Download or read book Bayesian Inference of State Space Models written by Kostas Triantafyllopoulos and published by Springer Nature. This book was released on 2021-11-12 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models. The celebrated Kalman filter, with its numerous extensions, takes centre stage in the book. Univariate and multivariate models, linear Gaussian, non-linear and non-Gaussian models are discussed with applications to signal processing, environmetrics, economics and systems engineering. Over the past years there has been a growing literature on Bayesian inference of state space models, focusing on multivariate models as well as on non-linear and non-Gaussian models. The availability of time series data in many fields of science and industry on the one hand, and the development of low-cost computational capabilities on the other, have resulted in a wealth of statistical methods aimed at parameter estimation and forecasting. This book brings together many of these methods, presenting an accessible and comprehensive introduction to state space models. A number of data sets from different disciplines are used to illustrate the methods and show how they are applied in practice. The R package BTSA, created for the book, includes many of the algorithms and examples presented. The book is essentially self-contained and includes a chapter summarising the prerequisites in undergraduate linear algebra, probability and statistics. An up-to-date and complete account of state space methods, illustrated by real-life data sets and R code, this textbook will appeal to a wide range of students and scientists, notably in the disciplines of statistics, systems engineering, signal processing, data science, finance and econometrics. With numerous exercises in each chapter, and prerequisite knowledge conveniently recalled, it is suitable for upper undergraduate and graduate courses.