Kalman Filter
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| Author | : Charles K. Chui |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 209 |
| Release | : 2013-06-29 |
| Genre | : Science |
| ISBN | : 366202666X |
Download Kalman Filtering Book in PDF, Epub and Kindle
In addition to making a number of minor corrections and updat ing the references, we have expanded the section on "real-time system identification" in Chapter 10 of the first edition into two sections and combined it with Chapter 8. In its place, a very brief introduction to wavelet analysis is included in Chapter 10. Although the pyramid algorithms for wavelet decompositions and reconstructions are quite different from the Kalman filtering al gorithms, they can also be applied to time-domain filtering, and it is hoped that splines and wavelets can be incorporated with Kalman filtering in the near future. College Station and Houston Charles K. Chui September 1990 Guanrong Chen Preface to the First Edition 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. 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 fire control. With the recent development of high-speed computers, the Kalman filter has become more use ful even for very complicated real-time applications.
| Author | : Branko Ristic |
| Publisher | : Artech House |
| Total Pages | : 328 |
| Release | : 2003-12-01 |
| Genre | : Technology & Engineering |
| ISBN | : 9781580538510 |
Download Beyond the Kalman Filter: Particle Filters for Tracking Applications Book in PDF, Epub and Kindle
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.
| Author | : Robert Grover Brown |
| Publisher | : Wiley-Liss |
| Total Pages | : 504 |
| Release | : 1997 |
| Genre | : Computers |
| ISBN | : |
Download Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises and Solutions Book in PDF, Epub and Kindle
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.
| Author | : Simon Haykin |
| Publisher | : John Wiley & Sons |
| Total Pages | : 302 |
| Release | : 2004-03-24 |
| Genre | : Technology & Engineering |
| ISBN | : 047146421X |
Download Kalman Filtering and Neural Networks Book in PDF, Epub and Kindle
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.
| Author | : Paul Zarchan |
| Publisher | : AIAA (American Institute of Aeronautics & Astronautics) |
| Total Pages | : 0 |
| Release | : 2009 |
| Genre | : Aeronautics |
| ISBN | : 9781600867187 |
Download Fundamentals of Kalman Filtering Book in PDF, Epub and Kindle
Numerical basics -- Method of least squares -- Recursive least-squares filtering -- Polynomial Kalman filters -- Kalman filters in a nonpolynomial world -- Continuous polynomial Kalman filter -- Extended Kalman filtering -- Drag and falling object -- Cannon-launched projectile tracking problem -- Tracking a sine wave -- Satellite navigation -- Biases -- Linearized Kalman filtering -- Miscellaneous topics -- Fading-memory filter -- Assorted techniques for improving Kalman-filter performance -- Fixed-memory filters -- Chain-rule and least-squares filtering -- Filter bank approach to tracking a sine wave -- Appendix A: Fundamentals of Kalman-filtering software -- Appendix B: Key formula and concept summary
| Author | : Felix Govaers |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 130 |
| Release | : 2019-05-22 |
| Genre | : Computers |
| ISBN | : 1838805362 |
Download Introduction and Implementations of the Kalman Filter Book in PDF, Epub and Kindle
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.
| Author | : Mohinder S. Grewal |
| Publisher | : John Wiley & Sons |
| Total Pages | : 639 |
| Release | : 2015-02-02 |
| Genre | : Technology & Engineering |
| ISBN | : 111898496X |
Download Kalman Filtering Book in PDF, Epub and Kindle
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 | : Randall L. Eubank |
| Publisher | : CRC Press |
| Total Pages | : 199 |
| Release | : 2005-11-29 |
| Genre | : Mathematics |
| ISBN | : 1420028677 |
Download A Kalman Filter Primer Book in PDF, Epub and Kindle
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 notation
| Author | : Bruce P. Gibbs |
| Publisher | : John Wiley & Sons |
| Total Pages | : 559 |
| Release | : 2011-03-29 |
| Genre | : Technology & Engineering |
| ISBN | : 1118003160 |
Download Advanced Kalman Filtering, Least-Squares and Modeling Book in PDF, Epub and Kindle
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.
| Author | : C. Wells |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 181 |
| Release | : 2013-03-09 |
| Genre | : Business & Economics |
| ISBN | : 940158611X |
Download The Kalman Filter in Finance Book in PDF, Epub and Kindle
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.
