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Author: Badong Chen Publisher: Springer Nature ISBN: 3031337646 Category : Technology & Engineering Languages : en Pages : 304
Book Description
This book provides several efficient Kalman filters (linear or nonlinear) under information theoretic criteria. They achieve excellent performance in complicated non-Gaussian noises with low computation complexity and have great practical application potential. The book combines all these perspectives and results in a single resource for students and practitioners in relevant application fields. Each chapter starts with a brief review of fundamentals, presents the material focused on the most important properties and evaluates comparatively the models discussing free parameters and their effect on the results. Proofs are provided at the end of each chapter. The book is geared to senior undergraduates with a basic understanding of linear algebra, signal processing and statistics, as well as graduate students or practitioners with experience in Kalman filtering.
Author: Badong Chen Publisher: Springer Nature ISBN: 3031337646 Category : Technology & Engineering Languages : en Pages : 304
Book Description
This book provides several efficient Kalman filters (linear or nonlinear) under information theoretic criteria. They achieve excellent performance in complicated non-Gaussian noises with low computation complexity and have great practical application potential. The book combines all these perspectives and results in a single resource for students and practitioners in relevant application fields. Each chapter starts with a brief review of fundamentals, presents the material focused on the most important properties and evaluates comparatively the models discussing free parameters and their effect on the results. Proofs are provided at the end of each chapter. The book is geared to senior undergraduates with a basic understanding of linear algebra, signal processing and statistics, as well as graduate students or practitioners with experience in Kalman filtering.
Author: Adrian Pizzinga Publisher: Springer Science & Business Media ISBN: 1461447380 Category : Mathematics Languages : en Pages : 66
Book Description
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).
Author: Mohinder S. Grewal Publisher: John Wiley & Sons ISBN: 111898496X Category : Technology & Engineering Languages : en Pages : 639
Book Description
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.
Author: Charles K. Chui Publisher: Springer Science & Business Media ISBN: 3540878483 Category : Business & Economics Languages : en Pages : 241
Book Description
This book presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method and an indirect method.
Author: Guanrong Chen Publisher: World Scientific ISBN: 9789810213596 Category : Computers Languages : en Pages : 248
Book Description
Kalman filtering algorithm gives optimal (linear, unbiased and minimum error-variance) estimates of the unknown state vectors of a linear dynamic-observation system, under the regular conditions such as perfect data information; complete noise statistics; exact linear modeling; ideal well-conditioned matrices in computation and strictly centralized filtering.In practice, however, one or more of the aforementioned conditions may not be satisfied, so that the standard Kalman filtering algorithm cannot be directly used, and hence ?approximate Kalman filtering? becomes necessary. In the last decade, a great deal of attention has been focused on modifying and/or extending the standard Kalman filtering technique to handle such irregular cases. It has been realized that approximate Kalman filtering is even more important and useful in applications.This book is a collection of several tutorial and survey articles summarizing recent contributions to the field, along the line of approximate Kalman filtering with emphasis on both its theoretical and practical aspects.
Author: P.A. Ruymgaart Publisher: Springer Science & Business Media ISBN: 3642968422 Category : Computers Languages : en Pages : 181
Book Description
Since their introduction in the mid 1950s, the filtering techniques developed by Kalman, and by Kalman and Bucy have been widely known and widely used in all areas of applied sciences. Starting with applications in aerospace engineering, their impact has been felt not only in all areas of engineering but also in the social sciences, biological sciences, medical sciences, as well as all other physical sciences. Despite all the good that has come out of this devel opment, however, there have been misuses because the theory has been used mainly as a tool or a procedure by many applied workers without them fully understanding its underlying mathematical workings. This book addresses a mathematical approach to Kalman-Bucy filtering and is an outgrowth of lectures given at our institutions since 1971 in a sequence of courses devoted to Kalman-Bucy filters. The material is meant to be a theoretical complement to courses dealing with applications and is designed for students who are well versed in the techniques of Kalman-Bucy filtering but who are also interested in the mathematics on which these may be based. The main topic addressed in this book is continuous-time Kalman-Bucy filtering. Although the discrete-time Kalman filter results were obtained first, the continuous-time results are important when dealing with systems developing in time continuously, which are hence more appropriately mod eled by differential equations than by difference equations. On the other hand, observations from the former can be obtained in a discrete fashion.
Author: C. K. Chui Publisher: Springer Science & Business Media ISBN: 9783540540137 Category : Kalman filtering Languages : en Pages : 195
Book Description
This book presents a thorough discussion of the mathematical theory of Kalman filtering. The filtering equations are derived in a series of elementary steps enabling the optimality of the process to be understood. It provides a comprehensive treatment of various major topics in Kalman-filtering theory, including uncorrelated and correlated noise, colored noise, steady-state theory, nonlinear systems, systems identification, numerical algorithms, and real-time applications. A series of problems for the student, together with a complete set of solutions, are also included. The style of the book is informal, and the mathematics elementary but rigorous, making it accessible to all those with a minimal knowledge of linear algebra and systems theory. In this second edition, in addition to some minor corrections and up-dating, the section on real-time system identification has been expanded and a brief introduction to wavelet analysis included.
Author: Danilo Comminiello Publisher: Butterworth-Heinemann ISBN: 0128129778 Category : Technology & Engineering Languages : en Pages : 390
Book Description
Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.
Author: Beata Akselsen Publisher: ISBN: 9781681172675 Category : Languages : en Pages : 308
Book Description
The Kalman filter is an algorithm that estimates the state of a system from measured data. It was predominantly developed by the Hungarian engineer Rudolf Kalman, for whom the filter is named. The filters algorithm is a two-step process: the first step predicts the state of the system, and the second step uses noisy measurements to refine the estimate of system state. There are now several variations of the original Kalman filter. The Kalman filter has plentiful applications in technology. A common application is for guidance, navigation and control of vehicles, particularly aircraft and spacecraft. Furthermore, the Kalman filter is a widely applied concept in time series analysis used in fields such as signal processing and econometrics. Kalman filters also are one of the main topics in the field of robotic motion planning and control, and they are sometimes included in trajectory optimization. Kalman filters are used for object tracking to predict an objects future location, to account for noise in an objects detected location, and to help associate multiple objects with their corresponding tracks. The output of the Kalman filter is denoted by the red circles and the object detection is denoted in black. Notice when the ball is occluded and there are no detections; the filter is used to predict its location. The purpose of the book entitled Kalman Filter Recent Advances and Applications is to provide an overview of recent developments in Kalman filter theory and their applications in engineering and scientific fields. This book corresponding to modern advances in Kalman filtering theory, solicitations in medical and biological sciences, tracking and positioning systems, electrical engineering and, finally, industrial processes and communication networks.
Author: Badong Chen
Author: Badong Chen
Author: Adrian Pizzinga
Author: A. V. Balakrishnan
Author: Mohinder S. Grewal
Author: Charles K. Chui
Author: Guanrong Chen
Author: P.A. Ruymgaart
Author: C. K. Chui
Author: Danilo Comminiello
Author: Beata Akselsen