Download or read book Reduced order Kalman Filter for a Class of Continuous Time Systems with Slow and Fast Modes written by Saif Almansouri and published by . This book was released on 2016 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, complete decomposition of the Kalman filter into the reduced-order Kalman filter with slow and fast modes is addressed. First, we investigate the decomposition so that the slow and fast filters are completely separated with both of filters driven by the system measurements. The simulation results are presented for such a decomposition using an aircraft example. In the second part, this thesis presents the design of reduced order Kalman filters for systems with both slow and fast modes for the case of perfect measurement. The main advantage of the reduced order approach is moderating and reducing mathematical difficulties to obtain the optimal state estimation. This will facilitate the use of Kalman filter for a class of real-time physical systems. In this thesis, we explain the effectiveness of the proposed design through theoretical studies and simulation results.
Download or read book Applied Mechanics Reviews written by and published by . This book was released on 1996 with total page 708 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Kalman Filter Design for Large scale Systems by Using Unified Approach written by Chen Jiang and published by . This book was released on 2013 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, a method of designing a Kalman filter for a linear, discrete-time, singularly perturbed stochastic system using the delta operator was introduced. This unified approach, which is based on the delta operator, was the main method used to unify the continuous-time system and the discrete-time system. This method has many advantages over the q-operator: it makes the system simpler, it has better finite word-length characteristics, and the truncation and round-off error is less. One of the singular perturbation techniques, quasi-steady state approximation, was used to separate the system into a slow subsystem and a fast subsystem. Then, the exact solution of the Kalman filter and the minimized mean square error for the full-order system, and the composite solution of the Kalman filter and minimized mean square error for the two subsystems were solved. This method was applied using a numerical example in which the steady-state Kalman filter solution was found.
Download or read book International Aerospace Abstracts written by and published by . This book was released on 1999 with total page 1020 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Kalman Filtering with Real Time Applications written by Charles K. Chui and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear, unbiased, and min imum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time intervals. It has been widely used in many areas of industrial and government applications such as video and laser tracking systems, satellite navigation, ballistic missile trajectory estimation, radar, and fue control. With the recent development of high-speed computers, the Kalman filter has become more use ful even for very complicated real-time applications. lnspite of its importance, the mathematical theory of Kalman filtering and its implications are not well understood even among many applied mathematicians and engineers. In fact, most prac titioners are just told what the filtering algorithms are without knowing why they work so well. One of the main objectives of this text is to disclose this mystery by presenting a fairly thor ough discussion of its mathematical theory and applications to various elementary real-time problems. A very elementary derivation of the filtering equations is fust presented. By assuming that certain matrices are nonsingular, the advantage of this approach is that the optimality of the Kalman filter can be easily understood. Of course these assump tions can be dropped by using the more well known method of orthogonal projection usually known as the innovations approach.
Download or read book Kalman Filters written by Ginalber Luiz Serra and published by BoD – Books on Demand. This book was released on 2018-02-21 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Approximate Kalman Filtering written by Guanrong Chen and published by World Scientific. This book was released on 1993-08-30 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book A Kalman Filter Primer written by Randall L. Eubank and published by CRC Press. This book was released on 2005-11-29 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Dissertation Abstracts International written by and published by . This book was released on 1985 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Introduction to Kalman Filtering with MATLAB Examples written by Narayan Kovvali and published by Morgan & Claypool Publishers. This book was released on 2013-09-01 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating the states of a dynamical system in which the state evolution and measurement processes are both linear and Gaussian. Given the ubiquity of such systems, the Kalman filter finds use in a variety of applications, e.g., target tracking, guidance and navigation, and communications systems. The purpose of this book is to present a brief introduction to Kalman filtering. The theoretical framework of the Kalman filter is first presented, followed by examples showing its use in practical applications. Extensions of the method to nonlinear problems and distributed applications are discussed. A software implementation of the algorithm in the MATLAB programming language is provided, as well as MATLAB code for several example applications discussed in the manuscript.
Download or read book Mathematics of Kalman Bucy Filtering written by P.A. Ruymgaart and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book On the Implementation of Reduced Sub optimal Kalman Filters for Discrete Linear Stochastic Processes with Time invariant Dynamics written by Jaun Francisco Lara and published by . This book was released on 1969 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Three different approaches to the problem of implementing a reduced-order, sub-optimal Kalman filter for a discrete, linear stochastic process, with time-invariant dynamics, are presented. A first method, A, is based upon the partitioning of the system dynamics. A second method, B, is implemented using matrix pseudo-inversion and a third method, C, is based upon reduction of the original process to one of lower order using the dominant roots of the system. An expression for the performance degradation in method A is derived. In method B, expression for the sub-optimal estimation error, and sub-optimal variance of estimation error are derived. The several methods are applied to a fourth-order process for illustration. (Author).
Download or read book Kalman Filtering Theory written by A. V. Balakrishnan and published by . This book was released on 1987 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Restricted Kalman Filtering written by Adrian Pizzinga and published by Springer Science & Business Media. This book was released on 2012-07-25 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).
Download or read book Mathematical Reviews written by and published by . This book was released on 1986 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Filtering Complex Turbulent Systems written by Andrew J. Majda and published by Cambridge University Press. This book was released on 2012-02-23 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authors develop a systematic applied mathematics perspective on the problems associated with filtering complex turbulent systems. The book contains background material from filtering, turbulence theory and numerical analysis, making it suitable for graduate courses as well as for researchers in a range of disciplines where applied mathematics is required.
Download or read book Introduction to Random Signals and Applied Kalman Filtering written by Robert Grover Brown and published by . This book was released on 1992 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on applied Kalman filtering and its random signal analysis. Important to all control system and communication engineers, it emphasizes applications, computer software and associated sets of special computer problems to aid in tying together both theory and practice. Along with actual case studies, a diskette is included to enable readers to actually see how Kalman filtering works.
