Author: J. S. Meditch
Publisher:
ISBN:
Category :
Languages : en
Pages : 18
Book Description
Kalman's formal limiting procedure is applied to some recent results in sequential discrete-time nonlinear filtering and smoothing to obtain the corresponding estimation algorithms for continuous-time nonlinear dynamic systems. The resulting filtering algorithm is found to agree with the well-known Detchmendy-Sridhar filter which was obtained via another method. The present smoothing algorithm is a new result. It is argued that the combined filter-smoothing results here lead to an estimation algorithm which is second-order in both system dynamics and measurement function nonlinearity. (Author).
Formal Algorithms for Continuous-time Nonlinear Filtering and Smoothing
Author: J. S. Meditch
Publisher:
ISBN:
Category :
Languages : en
Pages : 18
Book Description
Kalman's formal limiting procedure is applied to some recent results in sequential discrete-time nonlinear filtering and smoothing to obtain the corresponding estimation algorithms for continuous-time nonlinear dynamic systems. The resulting filtering algorithm is found to agree with the well-known Detchmendy-Sridhar filter which was obtained via another method. The present smoothing algorithm is a new result. It is argued that the combined filter-smoothing results here lead to an estimation algorithm which is second-order in both system dynamics and measurement function nonlinearity. (Author).
Publisher:
ISBN:
Category :
Languages : en
Pages : 18
Book Description
Kalman's formal limiting procedure is applied to some recent results in sequential discrete-time nonlinear filtering and smoothing to obtain the corresponding estimation algorithms for continuous-time nonlinear dynamic systems. The resulting filtering algorithm is found to agree with the well-known Detchmendy-Sridhar filter which was obtained via another method. The present smoothing algorithm is a new result. It is argued that the combined filter-smoothing results here lead to an estimation algorithm which is second-order in both system dynamics and measurement function nonlinearity. (Author).
Scientific and Technical Aerospace Reports
Nonlinear Filtering
Author: Jitendra R. Raol
Publisher: CRC Press
ISBN: 1498745180
Category : Technology & Engineering
Languages : en
Pages : 581
Book Description
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.
Publisher: CRC Press
ISBN: 1498745180
Category : Technology & Engineering
Languages : en
Pages : 581
Book Description
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.
Applied Mechanics Reviews
Nonlinear Filters
Author: Peyman Setoodeh
Publisher: John Wiley & Sons
ISBN: 1119078156
Category : Technology & Engineering
Languages : en
Pages : 308
Book Description
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.
Publisher: John Wiley & Sons
ISBN: 1119078156
Category : Technology & Engineering
Languages : en
Pages : 308
Book Description
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.
U. S. Government Research and Development Reports
U.S. Government Research & Development Reports
State Estimation for Nonlinear Systems Via Quasilinearization
University of Michigan Official Publication
Author: University of Michigan
Publisher: UM Libraries
ISBN:
Category : Education, Higher
Languages : en
Pages : 1080
Book Description
Each number is the catalogue of a specific school or college of the University.
Publisher: UM Libraries
ISBN:
Category : Education, Higher
Languages : en
Pages : 1080
Book Description
Each number is the catalogue of a specific school or college of the University.
Estimation Theory
Author: Demetrios G. Lainiotis
Publisher: Elsevier Publishing Company
ISBN:
Category : Mathematics
Languages : en
Pages : 192
Book Description
Publisher: Elsevier Publishing Company
ISBN:
Category : Mathematics
Languages : en
Pages : 192
Book Description