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Author: Richard S. Bucy
Publisher:
ISBN:
Category : Computer architecture
Languages : en
Pages : 106
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Book Description
Examples of two and three dimensional phase demodulation problems are presented. Computer realizations for the optimal nonlinear phase estimator are discussed in detail, with emphasis on parallel computer architectures. Implementation of the nonlinear filter on various computer architectures, including the CDC6600/7600, CDC STAR-100, Illiac IV, the CRAY-1, and the Floating Point System AP120B is reviewed. Detailed Monte Carlo performance analysis is presented for the two-dimensional system, while partial results are included for the three dimensional case. Implications concerning the ideal computer architecture for nonlinear filter realization are discussed. (Author).
Author: Richard S. Bucy
Publisher:
ISBN:
Category : Computer architecture
Languages : en
Pages : 106
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Book Description
Examples of two and three dimensional phase demodulation problems are presented. Computer realizations for the optimal nonlinear phase estimator are discussed in detail, with emphasis on parallel computer architectures. Implementation of the nonlinear filter on various computer architectures, including the CDC6600/7600, CDC STAR-100, Illiac IV, the CRAY-1, and the Floating Point System AP120B is reviewed. Detailed Monte Carlo performance analysis is presented for the two-dimensional system, while partial results are included for the three dimensional case. Implications concerning the ideal computer architecture for nonlinear filter realization are discussed. (Author).
Author: Peyman Setoodeh
Publisher: John Wiley & Sons
ISBN: 1119078156
Category : Technology & Engineering
Languages : en
Pages : 308
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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.
Author: Sueo Sugimoto
Publisher: Ohmsha, Ltd.
ISBN: 4274805026
Category : Mathematics
Languages : en
Pages : 457
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Book Description
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: Khaled Salah Mohamed
Publisher: Springer Nature
ISBN: 303058996X
Category : Technology & Engineering
Languages : en
Pages : 104
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Book Description
This book provides comprehensive coverage of various Cryptography topics, while highlighting the most recent trends such as quantum, blockchain, lightweight, Chaotic and DNA cryptography. Moreover, this book covers cryptography primitives and its usage and applications and focuses on the fundamental principles of modern cryptography such as Stream Ciphers, block ciphers, public key algorithms and digital signatures. Readers will gain a solid foundation in cryptography and security. This book presents the fundamental mathematical concepts of cryptography. Moreover, this book presents hiding data techniques such as steganography and watermarking. The author also provides a comparative study of the different cryptographic methods, which can be used to solve security problems.
Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 880
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Book Description
Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
Author: Ronald K. Pearson
Publisher: CRC Press
ISBN: 1498714137
Category : Medical
Languages : en
Pages : 286
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Book Description
Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Adopting both structural and behavioral approaches in characterizing and designing nonlinear digital filters, this book: Begins with an expedient introduction to programming in the free, open-source computing environment of Python Uses results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classes Analyzes the impact of a range of useful interconnection strategies on filter behavior, providing Python implementations of the presented filters and interconnection strategies Proposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these components Illustrates the behavioral consequences of allowing recursive (i.e., feedback) interconnections in nonlinear digital filters while highlighting a challenging but promising research frontier Nonlinear Digital Filtering with Python: An Introduction supplies essential knowledge useful for developing and implementing data cleaning filters for dynamic data analysis and time-series modeling.
Author: Jitendra R. Raol
Publisher: CRC Press
ISBN: 1351647954
Category : Technology & Engineering
Languages : en
Pages : 1079
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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.
Author: Chin-Pang Alex Fung
Publisher:
ISBN:
Category :
Languages : en
Pages : 240
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Book Description
Author: Nasir Uddin Ahmed
Publisher: World Scientific
ISBN: 9789810236090
Category : Technology & Engineering
Languages : en
Pages : 280
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Book Description
"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: Hisashi Tanizaki
Publisher: Springer Science & Business Media
ISBN: 366222237X
Category : Business & Economics
Languages : en
Pages : 215
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Book Description
For a nonlinear filtering problem, the most heuristic and easiest approximation is to use the Taylor series expansion and apply the conventional linear recursive Kalman filter algorithm directly to the linearized nonlinear measurement and transition equations. First, it is discussed that the Taylor series expansion approach gives us the biased estimators. Next, a Monte-Carlo simulation filter is proposed, where each expectation of the nonlinear functions is evaluated generating random draws. It is shown from Monte-Carlo experiments that the Monte-Carlo simulation filter yields the unbiased but inefficient estimator. Anotherapproach to the nonlinear filtering problem is to approximate the underlyingdensity functions of the state vector. In this monograph, a nonlinear and nonnormal filter is proposed by utilizing Monte-Carlo integration, in which a recursive algorithm of the weighting functions is derived. The densityapproximation approach gives us an asymptotically unbiased estimator. Moreover, in terms of programming and computational time, the nonlinear filter using Monte-Carlo integration can be easily extended to higher dimensional cases, compared with Kitagawa's nonlinear filter using numericalintegration.
