Kalman Filtering

Kalman Filtering
Author: Charles K. Chui
Publisher: Springer Science & Business Media
Total Pages: 209
Release: 2013-06-29
Genre: Science
ISBN: 366202666X
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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.

Kalman Filtering

Kalman Filtering
Author: Mohinder S. Grewal
Publisher: John Wiley & Sons
Total Pages: 639
Release: 2015-02-02
Genre: Technology & Engineering
ISBN: 111898496X
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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 Filtering and Neural Networks

Kalman Filtering and Neural Networks
Author: Simon Haykin
Publisher: John Wiley & Sons
Total Pages: 302
Release: 2004-03-24
Genre: Technology & Engineering
ISBN: 047146421X
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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.

Tracking and Kalman Filtering Made Easy

Tracking and Kalman Filtering Made Easy
Author: Eli Brookner
Publisher: Wiley-Interscience
Total Pages: 512
Release: 1998
Genre: Technology & Engineering
ISBN:
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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.

Advanced Kalman Filtering, Least-Squares and Modeling

Advanced Kalman Filtering, Least-Squares and Modeling
Author: Bruce P. Gibbs
Publisher: John Wiley & Sons
Total Pages: 559
Release: 2011-03-29
Genre: Technology & Engineering
ISBN: 1118003160
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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.

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
Publisher: Wiley-Liss
Total Pages: 504
Release: 1997
Genre: Computers
ISBN:
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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.

Fundamentals of Kalman Filtering

Fundamentals of Kalman Filtering
Author: Paul Zarchan
Publisher: AIAA (American Institute of Aeronautics & Astronautics)
Total Pages: 0
Release: 2009
Genre: Aeronautics
ISBN: 9781600867187
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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

Beyond the Kalman Filter: Particle Filters for Tracking Applications

Beyond the Kalman Filter: Particle Filters for Tracking Applications
Author: Branko Ristic
Publisher: Artech House
Total Pages: 328
Release: 2003-12-01
Genre: Technology & Engineering
ISBN: 9781580538510
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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.

Kalman Filtering and Information Fusion

Kalman Filtering and Information Fusion
Author: Hongbin Ma
Publisher: Springer Nature
Total Pages: 295
Release: 2019-11-27
Genre: Technology & Engineering
ISBN: 9811508062
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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.