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Master's Thesis from the year 2006 in the subject Geography / Earth Science - Cartography, Geographic Information Science and Geodesy, grade: 1.3, University of Applied Sciences Karlsruhe (Geomatik), course: Master of Geomatics, language: English, abstract: As further development of GOCA- (GNSS/LPS/LS-based Online Control and Alarm Systems) software, the Kalman filter was developed as additional module to monitor besides pure object point displacement also the velocity and the acceleration in a specified time interval. In this Master thesis the Kalman filter algorithm is modified, and additional capabilities are added. The additional capabilities include; first, a forecasting of expected displacement, velocity and acceleration to future. Second, computing the time at which the point displacement and velocity is expected to exceed the given critical values. Two estimation algorithms are used in the GOCA-Kalman filtering; first, least squares adjustment (L2-norm estimation). Second, L1-estimation. Data analysis of given projects were to be carried out and compared using both adjustment algorithms. To design and develop the GOCA-Kalman filter four steps are applied; first step, the GOCA-Kalman filter is realized and tested using MATLAB to create the mathematical algorithm and test the results of standard point given displacement, e.g. constant velocity displacement , parabola displacement, etc . Second step, a VC++ dynamic link library (.dll) is created. Third step, the DLL file was embedded in the GOCA software by calling the DLL file and its related libraries. And forth step, the Kalman filter graphics part had to be modified to show the state vector components (displacement, velocity, and acceleration) with their standard deviations, and additional the forecasted value and its standard deviation would be shown in the graphics part. Additional work is added to this master thesis to make artificial displacement GKA-files (GNSS/LPS/LS input files in the GOCA-software), where points displacements with linear, parabola etc are created. The software was realized using MATLAB GUI and named GKA-create.
With the increasing complexity of processes to be analyzed, the modern control engineer often needs to develop a model of the system to be controlled. However, in many cases, there is limited time for detailed system analysis, and the engineer may not be an expert in that particular domain. This work takes an engineering approach to bond graph modelling of dynamic systems, and provides an in-depth study of causality in the context of physical system modelling.
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.
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.
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.
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.
This book provides readers with a solid introduction to the theoretical and practical aspects of Kalman filtering. It has been updated with the latest developments in the implementation and application of Kalman filtering, including adaptations for nonlinear filtering, more robust smoothing methods, and developing applications in navigation. All software is provided in MATLAB, giving readers the opportunity to discover how the Kalman filter works in action and to consider the practical arithmetic needed to preserve the accuracy of results. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department -- to obtain the manual, send an email to [email protected].
The emergence of affordable micro sensors, such as MEMS Inertial Measurement Systems, which are being applied in embedded systems and Internet-of-Things devices, has brought techniques such as Kalman Filtering, capable of combining information from multiple sensors or sources, to the interest of students and hobbyists. This will book will develop just the necessary background concepts, helping a much wider audience of readers develop an understanding and intuition that will enable them to follow the explanation for the Kalman Filtering algorithm
This work is intended primarily as a handbook for engineers who must design practical systems. It discusses model development in detail so that the reader may design an estimator that meets all application requirements and is robust to modelling assumptions.