Download or read book Tracking and Kalman Filtering Made Easy written by Eli Brookner and published by Wiley-Interscience. This book was released on 1998 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Advanced Kalman Filtering Least Squares and Modeling written by Bruce P. Gibbs and published by John Wiley & Sons. This book was released on 2011-03-29 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Kalman Filtering written by Mohinder S. Grewal and published by John Wiley & Sons. This book was released on 2015-02-02 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Introduction and Implementations of the Kalman Filter written by Felix Govaers and published by BoD – Books on Demand. This book was released on 2019-05-22 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Estimation with Applications to Tracking and Navigation written by Yaakov Bar-Shalom and published by John Wiley & Sons. This book was released on 2004-04-05 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics. The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimation with a focus on applying these techniques to real systems. Other features include: * Problems that apply theoretical material to real-world applications * In-depth coverage of the Interacting Multiple Model (IMM) estimator * Companion DynaEst(TM) software for MATLAB(TM) implementation of Kalman filters and IMM estimators * Design guidelines for tracking filters Suitable for graduate engineering students and engineers working in remote sensors and tracking, Estimation with Applications to Tracking and Navigation provides expert coverage of this important area.
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 Neural information processing electronic resource written by Nikil R. Pal and published by Springer Science & Business Media. This book was released on 2004-11-18 with total page 1397 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation This book constitutes the refereed proceedings of the 11th International Conference on Neural Information Processing, ICONIP 2004, held in Calcutta, India in November 2004. The 186 revised papers presented together with 24 invited contributions were carefully reviewed and selected from 470 submissions. The papers are organized in topical sections on computational neuroscience, complex-valued neural networks, self-organizing maps, evolutionary computation, control systems, cognitive science, adaptive intelligent systems, biometrics, brain-like computing, learning algorithms, novel neural architectures, image processing, pattern recognition, neuroinformatics, fuzzy systems, neuro-fuzzy systems, hybrid systems, feature analysis, independent component analysis, ant colony, neural network hardware, robotics, signal processing, support vector machine, time series prediction, and bioinformatics.
Download or read book Kalman Filter for Beginners written by Pʻir-yŏng Kim and published by Createspace Independent Publishing Platform. This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dwarfs your fear towards complicated mathematical derivations and proofs. Experience Kalman filter with hands-on examples to grasp the essence. A book long awaited by anyone who could not dare to put their first step into Kalman filter. The author presents Kalman filter and other useful filters without complicated mathematical derivation and proof but with hands-on examples in MATLAB that will guide you step-by-step. The book starts with recursive filter and basics of Kalman filter, and gradually expands to application for nonlinear systems through extended and unscented Kalman filters. Also, some topics on frequency analysis including complementary filter are covered. Each chapter is balanced with theoretical background for absolute beginners and practical MATLAB examples to experience the principles explained. Once grabbing the book, you will notice it is not fearful but even enjoyable to learn Kalman filter.
Download or read book Kalman Filter written by Víctor M. Moreno and published by BoD – Books on Demand. This book was released on 2009-04-01 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to provide an overview of recent developments in Kalman filter theory and their applications in engineering and scientific fields. The book is divided into 24 chapters and organized in five blocks corresponding to recent advances in Kalman filtering theory, applications in medical and biological sciences, tracking and positioning systems, electrical engineering and, finally, industrial processes and communication networks.
Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2013-09-05 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Download or read book Kalman Filtering written by Mohinder S. Grewal and published by Wiley-Interscience. This book was released on 2001-01-16 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: Disk contains: Demonstation programs and source code in MATLAB for algorithms in text.
Download or read book Modeling Estimation and Optimal Filtration in Signal Processing written by Mohamed Najim and published by John Wiley & Sons. This book was released on 2010-01-05 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing. Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed. Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented. Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and their variants.
Download or read book Fundamentals of Kalman Filtering written by Paul Zarchan and published by AIAA (American Institute of Aeronautics & Astronautics). This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Optimal Estimation of Dynamic Systems written by John L. Crassidis and published by CRC Press. This book was released on 2004-04-27 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most newcomers to the field of linear stochastic estimation go through a difficult process in understanding and applying the theory.This book minimizes the process while introducing the fundamentals of optimal estimation. Optimal Estimation of Dynamic Systems explores topics that are important in the field of control where the signals received are used to determine highly sensitive processes such as the flight path of a plane, the orbit of a space vehicle, or the control of a machine. The authors use dynamic models from mechanical and aerospace engineering to provide immediate results of estimation concepts with a minimal reliance on mathematical skills. The book documents the development of the central concepts and methods of optimal estimation theory in a manner accessible to engineering students, applied mathematicians, and practicing engineers. It includes rigorous theoretial derivations and a significant amount of qualitiative discussion and judgements. It also presents prototype algorithms, giving detail and discussion to stimulate development of efficient computer programs and intelligent use of them. This book illustrates the application of optimal estimation methods to problems with varying degrees of analytical and numercial difficulty. It compares various approaches to help develop a feel for the absolute and relative utility of different methods, and provides many applications in the fields of aerospace, mechanical, and electrical engineering.
Download or read book Kalman Filtering and Information Fusion written by Hongbin Ma and published by Springer Nature. This book was released on 2019-11-27 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Probabilistic Robotics written by Sebastian Thrun and published by MIT Press. This book was released on 2005-08-19 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.
Download or read book Kalman Filter Made Easy written by William Franklin (Engineer) and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
