A New Suboptimal Kalman Filtering Algorithm For Hybrid Nonlinear Systems
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A New Suboptimal Kalman Filtering Algorithm For Hybrid Nonlinear Systems
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Author : Zhengying Fan
language : en
Publisher:
Release Date : 2000
A New Suboptimal Kalman Filtering Algorithm For Hybrid Nonlinear Systems written by Zhengying Fan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Kalwan filtering categories.
Approximate Kalman Filtering
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Author : Guanrong Chen
language : en
Publisher: World Scientific
Release Date : 1993
Approximate Kalman Filtering written by Guanrong Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Computers categories.
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.
Fault Diagnosis Of Nonlinear Systems Using A Hybrid Approach
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Author : Ehsan Sobhani-Tehrani
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-06-22
Fault Diagnosis Of Nonlinear Systems Using A Hybrid Approach written by Ehsan Sobhani-Tehrani and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-22 with Technology & Engineering categories.
Theincreasingcomplexityofspacevehiclessuchassatellites,andthecostreduction measures that have affected satellite operators are increasingly driving the need for more autonomy in satellite diagnostics and control systems. Current methods for detecting and correcting anomalies onboard the spacecraft as well as on the ground are primarily manual and labor intensive, and therefore, tend to be slow. Operators inspect telemetry data to determine the current satellite health. They use various statisticaltechniques andmodels,buttheanalysisandevaluation ofthelargevolume of data still require extensive human intervention and expertise that is prone to error. Furthermore, for spacecraft and most of these satellites, there can be potentially unduly long delays in round-trip communications between the ground station and the satellite. In this context, it is desirable to have onboard fault-diagnosis system that is capable of detecting, isolating, identifying or classifying faults in the system withouttheinvolvementandinterventionofoperators.Towardthisend,theprinciple goal here is to improve the ef?ciency, accuracy, and reliability of the trend analysis and diagnostics techniques through utilization of intelligent-based and hybrid-based methodologies.
Beyond The Kalman Filter Particle Filters For Tracking Applications
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Author : Branko Ristic
language : en
Publisher: Artech House
Release Date : 2003-12-01
Beyond The Kalman Filter Particle Filters For Tracking Applications written by Branko Ristic and has been published by Artech House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-12-01 with Technology & Engineering categories.
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.
Multisensor Attitude Estimation
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Author : Hassen Fourati
language : en
Publisher: CRC Press
Release Date : 2016-11-03
Multisensor Attitude Estimation written by Hassen Fourati and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-03 with Technology & Engineering categories.
There has been an increasing interest in multi-disciplinary research on multisensor attitude estimation technology driven by its versatility and diverse areas of application, such as sensor networks, robotics, navigation, video, biomedicine, etc. Attitude estimation consists of the determination of rigid bodies’ orientation in 3D space. This research area is a multilevel, multifaceted process handling the automatic association, correlation, estimation, and combination of data and information from several sources. Data fusion for attitude estimation is motivated by several issues and problems, such as data imperfection, data multi-modality, data dimensionality, processing framework, etc. While many of these problems have been identified and heavily investigated, no single data fusion algorithm is capable of addressing all the aforementioned challenges. The variety of methods in the literature focus on a subset of these issues to solve, which would be determined based on the application in hand. Historically, the problem of attitude estimation has been introduced by Grace Wahba in 1965 within the estimate of satellite attitude and aerospace applications. This book intends to provide the reader with both a generic and comprehensive view of contemporary data fusion methodologies for attitude estimation, as well as the most recent researches and novel advances on multisensor attitude estimation task. It explores the design of algorithms and architectures, benefits, and challenging aspects, as well as a broad array of disciplines, including: navigation, robotics, biomedicine, motion analysis, etc. A number of issues that make data fusion for attitude estimation a challenging task, and which will be discussed through the different chapters of the book, are related to: 1) The nature of sensors and information sources (accelerometer, gyroscope, magnetometer, GPS, inclinometer, etc.); 2) The computational ability at the sensors; 3) The theoretical developments and convergence proofs; 4) The system architecture, computational resources, fusion level.
Radar Data Processing With Applications
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Author : He You
language : en
Publisher: John Wiley & Sons
Release Date : 2016-10-24
Radar Data Processing With Applications written by He You and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-24 with Technology & Engineering categories.
Radar Data Processing with Applications Radar Data Processing with Applications He You, Xiu Jianjuan, Guan Xin, Naval Aeronautical and Astronautical University, China A summary of thirty years’ worth of research, this book is a systematic introduction to the theory, development, and latest research results of radar data processing technology. Highlights of the book include sections on data pre-processing technology, track initiation, and data association. Readers are also introduced to maneuvering target tracking, multiple target tracking termination, and track management theory. In order to improve data analysis, the authors have also included group tracking registration algorithms and a performance evaluation of radar data processing. Presents both classical theory and development methods of radar data processing Provides state-of-the-art research results, including data processing for modern radars and tracking performance evaluation theory Includes coverage of performance evaluation, registration algorithm for radar networks, data processing of passive radar, pulse Doppler radar, and phased array radar Features applications for those engaged in information engineering, radar engineering, electronic countermeasures, infrared techniques, sonar techniques, and military command Radar Data Processing with Applications is a handy guide for engineers and industry professionals specializing in the development of radar equipment and data processing. It is also intended as a reference text for electrical engineering graduate students and researchers specializing in signal processing and radars.
Optimal State Estimation
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Author : Dan Simon
language : en
Publisher: John Wiley & Sons
Release Date : 2006-06-19
Optimal State Estimation written by Dan Simon and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-19 with Technology & Engineering categories.
A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.
Stochastic Digital Control System Techniques
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Author :
language : en
Publisher: Academic Press
Release Date : 1996-05-16
Stochastic Digital Control System Techniques written by and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-05-16 with Technology & Engineering categories.
Praise for the Series:"This book will be a useful reference to control engineers and researchers. The papers contained cover well the recent advances in the field of modern control theory."-IEEE Group Correspondence"This book will help all those researchers who valiantly try to keep abreast of what is new in the theory and practice of optimal control."--Control
Scientific And Technical Aerospace Reports
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Author :
language : en
Publisher:
Release Date : 1990
Scientific And Technical Aerospace Reports written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Aeronautics categories.
Kalman Filtering
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Author : Charles K. Chui
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Kalman Filtering written by Charles K. Chui and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Science categories.
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.