New Numerical Algorithms for Nonlinear Filtering
Author | : Chin-Pang Alex Fung |
Publisher | : |
Total Pages | : 240 |
Release | : 1995 |
Genre | : |
ISBN | : |
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Author | : Chin-Pang Alex Fung |
Publisher | : |
Total Pages | : 240 |
Release | : 1995 |
Genre | : |
ISBN | : |
Author | : Justin Solomon |
Publisher | : CRC Press |
Total Pages | : 400 |
Release | : 2015-06-24 |
Genre | : Computers |
ISBN | : 1482251892 |
Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic desig
Author | : |
Publisher | : |
Total Pages | : 31 |
Release | : 1998 |
Genre | : |
ISBN | : |
The final report contains the outline of the research that was done during the period 1995-98. The main objective was to develop effective numerical algorithms of optimal nonlinear filtering and prediction and (more generally), state and parameter estimation in partially observed stochastic dynamical systems. During the course of the project a number of fundamental results were obtained, such as: development of a Wiener type optimal nonlinear filter (complete solution of "the last Wiener problem"); development of the spectral based approach to nonlinear filtering, which have led to the spectral separating scheme (separation of parameters and observations in optimal nonlinear filter) and other effective numerical approximations for the optimal nonlinear filter that include projection filter and assumed density filters. The results have been applied to specific "difficult" problems in target tracking, particularly, to the angle only tracking in EO and IR search and track systems and track-before-detect of resolved or sub-resolved low SNR targets. Extensive simulation showed that the proposed approach allows us to obtain much better performance as compared to the conventional expended Kalman filter in a number of important practical situations.
Author | : Sueo Sugimoto |
Publisher | : Ohmsha, Ltd. |
Total Pages | : 457 |
Release | : 2020-12-10 |
Genre | : Mathematics |
ISBN | : 4274805026 |
This book covers a broad range of filter theories, algorithms, and numerical examples. The representative linear and nonlinear filters such as the Kalman filter, the steady-state Kalman filter, the H infinity filter, the extended Kalman filter, the Gaussian sum filter, the statistically linearized Kalman filter, the unscented Kalman filter, the Gaussian filter, the cubature Kalman filter are first visited. Then, the non-Gaussian filters such as the ensemble Kalman filter and the particle filters based on the sequential Bayesian filter and the sequential importance resampling are described, together with their recent advances. Moreover, the information matrix in the nonlinear filtering, the nonlinear smoother based on the Markov Chain Monte Carlo, the continuous-discrete filters, factorized filters, and nonlinear filters based on stochastic approximation method are detailed. 1 Review of the Kalman Filter and Related Filters 2 Information Matrix in Nonlinear Filtering 3 Extended Kalman Filter and Gaussian Sum Filter 4 Statistically Linearized Kalman Filter 5 The Unscented Kalman Filter 6 General Gaussian Filters and Applications 7 The Ensemble Kalman Filter 8 Particle Filter 9 Nonlinear Smoother with Markov Chain Monte Carlo 10 Continuous-Discrete Filters 11 Factorized Filters 12 Nonlinear Filters Based on Stochastic Approximation Method
Author | : Yaakov Yavin |
Publisher | : Springer |
Total Pages | : 290 |
Release | : 1985 |
Genre | : Mathematics |
ISBN | : |
Author | : Nasir Uddin Ahmed |
Publisher | : World Scientific |
Total Pages | : 280 |
Release | : 1998 |
Genre | : Technology & Engineering |
ISBN | : 9789810236090 |
"many new results, especially on nonlinear filtering problems and their numerical techniques, are included in book form for the first time it will serve as a useful reference book on the recent progress in this field. The book can be used for teaching graduate courses to students in mathematics, probability, statistics, and engineering. And finally, doctoral students and young researchers in the area of filtering theory and its applications can find inspiring ideas and techniques".Journal of Applied Mathematics and Stochastic Analysis, 2000
Author | : Peyman Setoodeh |
Publisher | : John Wiley & Sons |
Total Pages | : 308 |
Release | : 2022-03-04 |
Genre | : Technology & Engineering |
ISBN | : 1119078156 |
NONLINEAR FILTERS Discover the utility of using deep learning and (deep) reinforcement learning in deriving filtering algorithms with this insightful and powerful new resource Nonlinear Filters: Theory and Applications delivers an insightful view on state and parameter estimation by merging ideas from control theory, statistical signal processing, and machine learning. Taking an algorithmic approach, the book covers both classic and machine learning-based filtering algorithms. Readers of Nonlinear Filters will greatly benefit from the wide spectrum of presented topics including stability, robustness, computability, and algorithmic sufficiency. Readers will also enjoy: Organization that allows the book to act as a stand-alone, self-contained reference A thorough exploration of the notion of observability, nonlinear observers, and the theory of optimal nonlinear filtering that bridges the gap between different science and engineering disciplines A profound account of Bayesian filters including Kalman filter and its variants as well as particle filter A rigorous derivation of the smooth variable structure filter as a predictor-corrector estimator formulated based on a stability theorem, used to confine the estimated states within a neighborhood of their true values A concise tutorial on deep learning and reinforcement learning A detailed presentation of the expectation maximization algorithm and its machine learning-based variants, used for joint state and parameter estimation Guidelines for constructing nonparametric Bayesian models from parametric ones Perfect for researchers, professors, and graduate students in engineering, computer science, applied mathematics, and artificial intelligence, Nonlinear Filters: Theory and Applications will also earn a place in the libraries of those studying or practicing in fields involving pandemic diseases, cybersecurity, information fusion, augmented reality, autonomous driving, urban traffic network, navigation and tracking, robotics, power systems, hybrid technologies, and finance.
Author | : Jitendra R. Raol |
Publisher | : CRC Press |
Total Pages | : 581 |
Release | : 2017-07-12 |
Genre | : Technology & Engineering |
ISBN | : 1498745180 |
Nonlinear Filtering covers linear and nonlinear filtering in a comprehensive manner, with appropriate theoretic and practical development. Aspects of modeling, estimation, recursive filtering, linear filtering, and nonlinear filtering are presented with appropriate and sufficient mathematics. A modeling-control-system approach is used when applicable, and detailed practical applications are presented to elucidate the analysis and filtering concepts. MATLAB routines are included, and examples from a wide range of engineering applications - including aerospace, automated manufacturing, robotics, and advanced control systems - are referenced throughout the text.
Author | : Kumar Pakki Bharani Chandra |
Publisher | : Springer |
Total Pages | : 184 |
Release | : 2018-11-20 |
Genre | : Technology & Engineering |
ISBN | : 3030017974 |
This book gives readers in-depth know-how on methods of state estimation for nonlinear control systems. It starts with an introduction to dynamic control systems and system states and a brief description of the Kalman filter. In the following chapters, various state estimation techniques for nonlinear systems are discussed, including the extended, unscented and cubature Kalman filters. The cubature Kalman filter and its variants are introduced in particular detail because of their efficiency and their ability to deal with systems with Gaussian and/or non-Gaussian noise. The book also discusses information-filter and square-root-filtering algorithms, useful for state estimation in some real-time control system design problems. A number of case studies are included in the book to illustrate the application of various nonlinear filtering algorithms. Nonlinear Filtering is written for academic and industrial researchers, engineers and research students who are interested in nonlinear control systems analysis and design. The chief features of the book include: dedicated coverage of recently developed nonlinear, Jacobian-free, filtering algorithms; examples illustrating the use of nonlinear filtering algorithms in real-world applications; detailed derivation and complete algorithms for nonlinear filtering methods, which help readers to a fundamental understanding and easier coding of those algorithms; and MATLAB® codes associated with case-study applications, which can be downloaded from the Springer Extra Materials website.
Author | : |
Publisher | : |
Total Pages | : 0 |
Release | : 2001 |
Genre | : |
ISBN | : |
In the research during the reporting period we focused on the following areas: (1) Nonlinear filtering for acutely maneuvering targets; (2) Development of banks of interacting Bayesian spatial-temporal matched filters for track-before-detect (TBD) based on nonlinear filtering techniques; (3) Development of adaptive spatial-temporal filters for clutter rejection and electronic scene stabilization; (4) Design of multi-hypothesis sequential tests for multi-sensor distributed systems with fusion of local decisions; (5) Wiener chaos expansion for nonlinear systems such with applications to filtering; and (6) Inverse problems for stochastic PDE. In addition. we have made substantial progress in the implementation of the developed algorithms. The Adaptive Spatial-Temporal Method for Clutter Rejection and Scene Stabilization and Switching Multiple Model Based TBD Algorithms were transferred to the SPAWAR Systems Center, San Diego, CA (POC: Dr.