Bayesian Bounds For Parameter Estimation And Nonlinear Filtering Tracking

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Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking

Author : Harry L. Van Trees
Publisher : Wiley-IEEE Press
ISBN 13 : 9780470120958
Total Pages : 951 pages
Book Rating : 4.1/5 (29 download)

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Book Synopsis Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking by : Harry L. Van Trees

Download or read book Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking written by Harry L. Van Trees and published by Wiley-IEEE Press. This book was released on 2007-08-31 with total page 951 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive development of Bayesian Bounds for parameter estimation and nonlinear filtering/tracking Bayesian estimation plays a central role in many signal processing problems encountered in radar, sonar, communications, seismology, and medical diagnosis. There are often highly nonlinear problems for which analytic evaluation of the exact performance is intractable. A widely used technique is to find bounds on the performance of any estimator and compare the performance of various estimators to these bounds. This book provides a comprehensive overview of the state of the art in Bayesian Bounds. It addresses two related problems: the estimation of multiple parameters based on noisy measurements and the estimation of random processes, either continuous or discrete, based on noisy measurements. An extensive introductory chapter provides an overview of Bayesian estimation and the interrelationship and applicability of the various Bayesian Bounds for both static parameters and random processes. It provides the context for the collection of papers that are included. This book will serve as a comprehensive reference for engineers and statisticians interested in both theory and application. It is also suitable as a text for a graduate seminar or as a supplementary reference for an estimation theory course.

Bayesian Filtering and Smoothing

Author : Simo Särkkä
Publisher : Cambridge University Press
ISBN 13 : 1108912303
Total Pages : 438 pages
Book Rating : 4.1/5 (89 download)

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Book Synopsis Bayesian Filtering and Smoothing by : Simo Särkkä

Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2023-05-31 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this accessible text presents a unified Bayesian treatment of state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models. The book focuses on discrete-time state space models and carefully introduces fundamental aspects related to optimal filtering and smoothing. In particular, it covers a range of efficient non-linear Gaussian filtering and smoothing algorithms, as well as Monte Carlo-based algorithms. This updated edition features new chapters on constructing state space models of practical systems, the discretization of continuous-time state space models, Gaussian filtering by enabling approximations, posterior linearization filtering, and the corresponding smoothers. Coverage of key topics is expanded, including extended Kalman filtering and smoothing, and parameter estimation. The book's practical, algorithmic approach assumes only modest mathematical prerequisites, suitable for graduate and advanced undergraduate students. Many examples are included, with Matlab and Python code available online, enabling readers to implement algorithms in their own projects.

Nonlinear Filters

Author : Peyman Setoodeh
Publisher : John Wiley & Sons
ISBN 13 : 1118835816
Total Pages : 308 pages
Book Rating : 4.1/5 (188 download)

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Book Synopsis Nonlinear Filters by : Peyman Setoodeh

Download or read book Nonlinear Filters written by Peyman Setoodeh and published by John Wiley & Sons. This book was released on 2022-04-12 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Bayesian Estimation and Tracking

Author : Anton J. Haug
Publisher : John Wiley & Sons
ISBN 13 : 1118287800
Total Pages : 400 pages
Book Rating : 4.1/5 (182 download)

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Book Synopsis Bayesian Estimation and Tracking by : Anton J. Haug

Download or read book Bayesian Estimation and Tracking written by Anton J. Haug and published by John Wiley & Sons. This book was released on 2012-05-29 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical approach to estimating and tracking dynamicsystems in real-worl applications Much of the literature on performing estimation for non-Gaussiansystems is short on practical methodology, while Gaussian methodsoften lack a cohesive derivation. Bayesian Estimation andTracking addresses the gap in the field on both accounts,providing readers with a comprehensive overview of methods forestimating both linear and nonlinear dynamic systems driven byGaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation andtracking, the book emphasizes the derivation of all trackingalgorithms within a Bayesian framework and describes effectivenumerical methods for evaluating density-weighted integrals,including linear and nonlinear Kalman filters for Gaussian-weightedintegrals and particle filters for non-Gaussian cases. The authorfirst emphasizes detailed derivations from first principles ofeeach estimation method and goes on to use illustrative anddetailed step-by-step instructions for each method that makescoding of the tracking filter simple and easy to understand. Case studies are employed to showcase applications of thediscussed topics. In addition, the book supplies block diagrams foreach algorithm, allowing readers to develop their own MATLAB®toolbox of estimation methods. Bayesian Estimation and Tracking is an excellent book forcourses on estimation and tracking methods at the graduate level.The book also serves as a valuable reference for researchscientists, mathematicians, and engineers seeking a deeperunderstanding of the topics.

Integrated Tracking, Classification, and Sensor Management

Author : Mahendra Mallick
Publisher : John Wiley & Sons
ISBN 13 : 1118450566
Total Pages : 569 pages
Book Rating : 4.1/5 (184 download)

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Book Synopsis Integrated Tracking, Classification, and Sensor Management by : Mahendra Mallick

Download or read book Integrated Tracking, Classification, and Sensor Management written by Mahendra Mallick and published by John Wiley & Sons. This book was released on 2012-11-05 with total page 569 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique guide to the state of the art of tracking, classification, and sensor management This book addresses the tremendous progress made over the last few decades in algorithm development and mathematical analysis for filtering, multi-target multi-sensor tracking, sensor management and control, and target classification. It provides for the first time an integrated treatment of these advanced topics, complete with careful mathematical formulation, clear description of the theory, and real-world applications. Written by experts in the field, Integrated Tracking, Classification, and Sensor Management provides readers with easy access to key Bayesian modeling and filtering methods, multi-target tracking approaches, target classification procedures, and large scale sensor management problem-solving techniques. Features include: An accessible coverage of random finite set based multi-target filtering algorithms such as the Probability Hypothesis Density filters and multi-Bernoulli filters with focus on problem solving A succinct overview of the track-oriented MHT that comprehensively collates all significant developments in filtering and tracking A state-of-the-art algorithm for hybrid Bayesian network (BN) inference that is efficient and scalable for complex classification models New structural results in stochastic sensor scheduling and algorithms for dynamic sensor scheduling and management Coverage of the posterior Cramer-Rao lower bound (PCRLB) for target tracking and sensor management Insight into cutting-edge military and civilian applications, including intelligence, surveillance, and reconnaissance (ISR) With its emphasis on the latest research results, Integrated Tracking, Classification, and Sensor Management is an invaluable guide for researchers and practitioners in statistical signal processing, radar systems, operations research, and control theory.

Bayesian Multiple Target Tracking, Second Edition

Author : Lawrence D. Stone
Publisher : Artech House
ISBN 13 : 1608075532
Total Pages : 315 pages
Book Rating : 4.6/5 (8 download)

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Book Synopsis Bayesian Multiple Target Tracking, Second Edition by : Lawrence D. Stone

Download or read book Bayesian Multiple Target Tracking, Second Edition written by Lawrence D. Stone and published by Artech House. This book was released on 2013-12-01 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition has undergone substantial revision from the 1999 first edition, recognizing that a lot has changed in the multiple target tracking field. One of the most dramatic changes is in the widespread use of particle filters to implement nonlinear, non-Gaussian Bayesian trackers. This book views multiple target tracking as a Bayesian inference problem. Within this framework it develops the theory of single target tracking, multiple target tracking, and likelihood ratio detection and tracking. In addition to providing a detailed description of a basic particle filter that implements the Bayesian single target recursion, this resource provides numerous examples that involve the use of particle filters. With these examples illustrating the developed concepts, algorithms, and approaches -- the book helps radar engineers develop tracking solutions when observations are non-linear functions of target state, when the target state distributions or measurement error distributions are not Gaussian, in low data rate and low signal to noise ratio situations, and when notions of contact and association are merged or unresolved among more than one target.

Poisson Point Processes

Author : Roy L. Streit
Publisher : Springer Science & Business Media
ISBN 13 : 1441969233
Total Pages : 274 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Poisson Point Processes by : Roy L. Streit

Download or read book Poisson Point Processes written by Roy L. Streit and published by Springer Science & Business Media. This book was released on 2010-09-15 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Poisson Point Processes provides an overview of non-homogeneous and multidimensional Poisson point processes and their numerous applications. Readers will find constructive mathematical tools and applications ranging from emission and transmission computed tomography to multiple target tracking and distributed sensor detection, written from an engineering perspective. A valuable discussion of the basic properties of finite random sets is included. Maximum likelihood estimation techniques are discussed for several parametric forms of the intensity function, including Gaussian sums, together with their Cramer-Rao bounds. These methods are then used to investigate: -Several medical imaging techniques, including positron emission tomography (PET), single photon emission computed tomography (SPECT), and transmission tomography (CT scans) -Various multi-target and multi-sensor tracking applications, -Practical applications in areas like distributed sensing and detection, -Related finite point processes such as marked processes, hard core processes, cluster processes, and doubly stochastic processes, Perfect for researchers, engineers and graduate students working in electrical engineering and computer science, Poisson Point Processes will prove to be an extremely valuable volume for those seeking insight into the nature of these processes and their diverse applications.

Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering

Author : Marcelo G. S. Bruno
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1627051201
Total Pages : 101 pages
Book Rating : 4.6/5 (27 download)

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Book Synopsis Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering by : Marcelo G. S. Bruno

Download or read book Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering written by Marcelo G. S. Bruno and published by Morgan & Claypool Publishers. This book was released on 2013-01-01 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: In these notes, we introduce particle filtering as a recursive importance sampling method that approximates the minimum-mean-square-error (MMSE) estimate of a sequence of hidden state vectors in scenarios where the joint probability distribution of the states and the observations is non-Gaussian and, therefore, closed-form analytical expressions for the MMSE estimate are generally unavailable. We begin the notes with a review of Bayesian approaches to static (i.e., time-invariant) parameter estimation. In the sequel, we describe the solution to the problem of sequential state estimation in linear, Gaussian dynamic models, which corresponds to the well-known Kalman (or Kalman-Bucy) filter. Finally, we move to the general nonlinear, non-Gaussian stochastic filtering problem and present particle filtering as a sequential Monte Carlo approach to solve that problem in a statistically optimal way. We review several techniques to improve the performance of particle filters, including importance function optimization, particle resampling, Markov Chain Monte Carlo move steps, auxiliary particle filtering, and regularized particle filtering. We also discuss Rao-Blackwellized particle filtering as a technique that is particularly well-suited for many relevant applications such as fault detection and inertial navigation. Finally, we conclude the notes with a discussion on the emerging topic of distributed particle filtering using multiple processors located at remote nodes in a sensor network. Throughout the notes, we often assume a more general framework than in most introductory textbooks by allowing either the observation model or the hidden state dynamic model to include unknown parameters. In a fully Bayesian fashion, we treat those unknown parameters also as random variables. Using suitable dynamic conjugate priors, that approach can be applied then to perform joint state and parameter estimation. Table of Contents: Introduction / Bayesian Estimation of Static Vectors / The Stochastic Filtering Problem / Sequential Monte Carlo Methods / Sampling/Importance Resampling (SIR) Filter / Importance Function Selection / Markov Chain Monte Carlo Move Step / Rao-Blackwellized Particle Filters / Auxiliary Particle Filter / Regularized Particle Filters / Cooperative Filtering with Multiple Observers / Application Examples / Summary

Nonlinear Filters

Author : Peyman Setoodeh
Publisher : John Wiley & Sons
ISBN 13 : 1119078156
Total Pages : 308 pages
Book Rating : 4.1/5 (19 download)

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Book Synopsis Nonlinear Filters by : Peyman Setoodeh

Download or read book Nonlinear Filters written by Peyman Setoodeh and published by John Wiley & Sons. This book was released on 2022-03-04 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Statistical Signal Processing of Complex-Valued Data

Author : Peter J. Schreier
Publisher : Cambridge University Press
ISBN 13 : 1139487620
Total Pages : 331 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Statistical Signal Processing of Complex-Valued Data by : Peter J. Schreier

Download or read book Statistical Signal Processing of Complex-Valued Data written by Peter J. Schreier and published by Cambridge University Press. This book was released on 2010-02-04 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex-valued random signals are embedded in the very fabric of science and engineering, yet the usual assumptions made about their statistical behavior are often a poor representation of the underlying physics. This book deals with improper and noncircular complex signals, which do not conform to classical assumptions, and it demonstrates how correct treatment of these signals can have significant payoffs. The book begins with detailed coverage of the fundamental theory and presents a variety of tools and algorithms for dealing with improper and noncircular signals. It provides a comprehensive account of the main applications, covering detection, estimation, and signal analysis of stationary, nonstationary, and cyclostationary processes. Providing a systematic development from the origin of complex signals to their probabilistic description makes the theory accessible to newcomers. This book is ideal for graduate students and researchers working with complex data in a range of research areas from communications to oceanography.

Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023)

Author : Andrew D. Ball
Publisher : Springer Nature
ISBN 13 : 3031494210
Total Pages : 1186 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) by : Andrew D. Ball

Download or read book Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) written by Andrew D. Ball and published by Springer Nature. This book was released on with total page 1186 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Academic Press Library in Signal Processing

Author : Mats Viberg
Publisher : Academic Press
ISBN 13 : 0124116213
Total Pages : 1013 pages
Book Rating : 4.1/5 (241 download)

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Book Synopsis Academic Press Library in Signal Processing by : Mats Viberg

Download or read book Academic Press Library in Signal Processing written by Mats Viberg and published by Academic Press. This book was released on 2013-08-31 with total page 1013 pages. Available in PDF, EPUB and Kindle. Book excerpt: This third volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in array and statistical signal processing. With this reference source you will: Quickly grasp a new area of research Understand the underlying principles of a topic and its application Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved Quick tutorial reviews of important and emerging topics of research in array and statistical signal processing Presents core principles and shows their application Reference content on core principles, technologies, algorithms and applications Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic

Coherence

Author : David Ramírez
Publisher : Springer Nature
ISBN 13 : 3031133315
Total Pages : 495 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Coherence by : David Ramírez

Download or read book Coherence written by David Ramírez and published by Springer Nature. This book was released on 2023-01-01 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book organizes principles and methods of signal processing and machine learning into the framework of coherence. The book contains a wealth of classical and modern methods of inference, some reported here for the first time. General results are applied to problems in communications, cognitive radio, passive and active radar and sonar, multi-sensor array processing, spectrum analysis, hyperspectral imaging, subspace clustering, and related. The reader will find new results for model fitting; for dimension reduction in models and ambient spaces; for detection, estimation, and space-time series analysis; for subspace averaging; and for uncertainty quantification. Throughout, the transformation invariances of statistics are clarified, geometries are illuminated, and null distributions are given where tractable. Stochastic representations are emphasized, as these are central to Monte Carlo simulations. The appendices contain a comprehensive account of matrix theory, the SVD, the multivariate normal distribution, and many of the important distributions for coherence statistics. The book begins with a review of classical results in the physical and engineering sciences where coherence plays a fundamental role. Then least squares theory and the theory of minimum mean-squared error estimation are developed, with special attention paid to statistics that may be interpreted as coherence statistics. A chapter on classical hypothesis tests for covariance structure introduces the next three chapters on matched and adaptive subspace detectors. These detectors are derived from likelihood reasoning, but it is their geometries and invariances that qualify them as coherence statistics. A chapter on independence testing in space-time data sets leads to a definition of broadband coherence, and contains novel applications to cognitive radio and the analysis of cyclostationarity. The chapter on subspace averaging reviews basic results and derives an order-fitting rule for determining the dimension of an average subspace. These results are used to enumerate sources of acoustic and electromagnetic radiation and to cluster subspaces into similarity classes. The chapter on performance bounds and uncertainty quantification emphasizes the geometry of the Cramèr-Rao bound and its related information geometry.

Practical Geolocation for Electronic Warfare Using MATLAB

Author : Nicholas O'Donoughue
Publisher : Artech House
ISBN 13 : 1630818895
Total Pages : 220 pages
Book Rating : 4.6/5 (38 download)

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Book Synopsis Practical Geolocation for Electronic Warfare Using MATLAB by : Nicholas O'Donoughue

Download or read book Practical Geolocation for Electronic Warfare Using MATLAB written by Nicholas O'Donoughue and published by Artech House. This book was released on 2022-06-30 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text explores the practical realities that arise from the employment of geolocation for electronic warfare in real-world systems, including position of the target, errors in sensor position, orientation, or velocity, and the impact of repeated measurements over time. The problems solved in the book have direct relevance to accurately locating and tracking UAVs, planes, and ships. As a companion volume to the author’s previous book Emitter Detection and Geolocation for Electronic Warfare (Artech House, 2019), this book goes in depth on real-world complications that include: working within and converting between different coordinate systems, incorporation of prior information about targets, sensor uncertainties, the use of multiple snapshots over time, and estimating the current position and velocity of moving targets. The e-book version described here includes several links to software and videos that can be downloaded from the publicly available Git repository. The book also includes all MATLAB code necessary to develop novel algorithms that allow comparisons to classical techniques and enable you to account for errors in timing, position, velocity, or orientation of the sensors. With its unique and updated coverage of detailed geolocation techniques and data, and easy linkable access to additional software and videos, this is a must-have book for engineers and electronic warfare practitioners who need the best information available on the development or employment of geolocation algorithms. It is also a useful teaching resource for faculty and students in engineering departments covering RF signal processing topics, as well as anyone interested in novel applications of SDR’s and UAVs.

Recent Research in Control Engineering and Decision Making

Author : Olga Dolinina
Publisher : Springer
ISBN 13 : 3030120724
Total Pages : 771 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Recent Research in Control Engineering and Decision Making by : Olga Dolinina

Download or read book Recent Research in Control Engineering and Decision Making written by Olga Dolinina and published by Springer. This book was released on 2019-01-28 with total page 771 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the full papers and short monographs developed on the base of the refereed proceedings of the International Conference on Information Technologies: Information and Communication Technologies for Research and Industry (ICIT-2019), held in Saratov, Russia in February 2019. The book brings accepted papers which present new approaches and methods of solving problems in the sphere of control engineering and decision making for the various fields of studies: industry and research, ontology-based data simulation, smart city technologies, theory and use of digital signal processing, cognitive systems, robotics, cybernetics, automation control theory, image recognition technologies, and computer vision. Particular emphasis is laid on modern trends, new approaches, algorithms and methods in selected fields of interest. The presented papers were accepted after careful reviews made by at least three independent reviewers in a double-blind way. The acceptance level was about 60%. The chapters are organized thematically in several areas within the following tracks: • Models, Methods & Approaches in Decision Making Systems • Mathematical Modelling for Industry & Research • Smart City Technologies The conference is focused on development and globalization of information and communication technologies (ICT), methods of control engineering and decision making along with innovations and networking, ICT for sustainable development and technological change, and global challenges. Moreover, the ICIT-2019 served as a discussion area for the actual above-mentioned topics. The editors believe that the readers will find the proceedings interesting and useful for their own research work.

Digital Spectral Analysis

Author : Francis Castanié
Publisher : John Wiley & Sons
ISBN 13 : 1118601831
Total Pages : 297 pages
Book Rating : 4.1/5 (186 download)

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Book Synopsis Digital Spectral Analysis by : Francis Castanié

Download or read book Digital Spectral Analysis written by Francis Castanié and published by John Wiley & Sons. This book was released on 2013-02-04 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature. The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models. An entire chapter is devoted to the non-parametric methods most widely used in industry. High resolution methods are detailed in a further four chapters: spectral analysis by stationary time series modeling, minimum variance, and subspace-based estimators. Finally, advanced concepts are the core of the last four chapters: spectral analysis of non-stationary random signals, space time adaptive processing: irregularly sampled data processing, particle filtering and tracking of varying sinusoids. Suitable for students, engineers working in industry, and academics at any level, this book provides a rare complete overview of the spectral analysis domain.

Cooperative Robots and Sensor Networks 2015

Author : Anis Koubâa
Publisher : Springer
ISBN 13 : 3319182994
Total Pages : 278 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Cooperative Robots and Sensor Networks 2015 by : Anis Koubâa

Download or read book Cooperative Robots and Sensor Networks 2015 written by Anis Koubâa and published by Springer. This book was released on 2015-05-18 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compiles some of the latest research in cooperation between robots and sensor networks. Structured in twelve chapters, this book addresses fundamental, theoretical, implementation and experimentation issues. The chapters are organized into four parts namely multi-robots systems, data fusion and localization, security and dependability, and mobility.