Orthogonal Waveforms and Filter Banks for Future Communication Systems, Renfors, Markku
Автор: Mukund Padmanabhan; Kenneth W. Martin; G?bor P?cel Название: Feedback-Based Orthogonal Digital Filters ISBN: 0792396553 ISBN-13(EAN): 9780792396550 Издательство: Springer Рейтинг: Цена: 23508.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Develops the theory of a feedback-based orthogonal digital filter and examines several applications where the filter topology leads to a simple and efficient solution. This book presents the observer-based viewpoint of several signal processing problems, and shows that problems that are treated independently in the literature are in fact linked.
Автор: Abdelhadi, Ahmed Khawar, Awais Clancy, T. Charles Название: Mimo radar waveform design for spectrum sharing with cellular systems ISBN: 3319297236 ISBN-13(EAN): 9783319297231 Издательство: Springer Рейтинг: Цена: 9141.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book addresses a novel way to design radar waveforms that can enable spectrum sharing between radars and communication systems, without causing interference to communication systems, and at the same time achieving radar objectives of target detection, estimation, and tracking.
Автор: Mukund Padmanabhan; Kenneth W. Martin; G?bor P?cel Название: Feedback-Based Orthogonal Digital Filters ISBN: 1461285593 ISBN-13(EAN): 9781461285595 Издательство: Springer Рейтинг: Цена: 19591.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Feedback-Based Orthogonal Digital Filters: Theory, Applications, and Implementation develops the theory of a feedback-based orthogonal digital filter and examines several applications where the filter topology leads to a simple and efficient solution.
Описание: Non-Sinusoidal Orthogonal Functions in Systems and Control.- Hybrid Function (HF) and Its Properties.- Function Approximation via Hybrid Functions.- Integration and Differentiation Using HF Domain Operational Matrices.- One-Shot Operational Matrices for Integration.- Solution of Linear Differential Equations.- Convolution of Time Functions.- Time Invariant System Analysis: State Space Approach.- Time Varying System Analysis: State Space Approach.- Multi-Delay System Analysis: State Space Approach.- Time Invariant System Analysis: Method of Convolution.- System Identification using State Space Approach: Time Invariant Systems.- System Identification using State Space Approach: Time Varying Systems.- Time Invariant System Identification: via 'Deconvolution'.- System Identification: Parameter Estimation of Transfer Function.
Автор: Peter S.C. Heuberger; Paul M.J. van den Hof; Bo Wa Название: Modelling and Identification with Rational Orthogonal Basis Functions ISBN: 185233956X ISBN-13(EAN): 9781852339562 Издательство: Springer Рейтинг: Цена: 26122.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Models of dynamical systems are of great importance in almost all fields of science and engineering and specifically in control, signal processing and information science. A model is always only an approximation of a real phenomenon so that having an approximation theory which allows for the analysis of model quality is a substantial concern. The use of rational orthogonal basis functions to represent dynamical systems and stochastic signals can provide such a theory and underpin advanced analysis and efficient modelling. It also has the potential to extend beyond these areas to deal with many problems in circuit theory, telecommunications, systems, control theory and signal processing.
Nine international experts have contributed to this work to produce thirteen chapters that can be read independently or as a comprehensive whole with a logical line of reasoning:
Construction and analysis of generalized orthogonal basis function model structure;
System Identification in a time domain setting and related issues of variance, numerics, and uncertainty bounding;
System identification in the frequency domain;
Design issues and optimal basis selection;
Transformation and realization theory.
Modelling and Identification with Rational Orthogonal Basis Functions affords a self-contained description of the development of the field over the last 15 years, furnishing researchers and practising engineers working with dynamical systems and stochastic processes with a standard reference work.
Описание: Orthogonal Frequency Division Multiplexing for Wireless Communications is an edited volume with contributions by leading authorities in the subject of OFDM.
Автор: Peter S.C. Heuberger; Paul M.J. van den Hof; Bo Wa Название: Modelling and Identification with Rational Orthogonal Basis Functions ISBN: 1849969760 ISBN-13(EAN): 9781849969765 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Models of dynamical systems are of great importance in almost all fields of science and engineering and specifically in control, signal processing and information science. A model is always only an approximation of a real phenomenon so that having an approximation theory which allows for the analysis of model quality is a substantial concern. The use of rational orthogonal basis functions to represent dynamical systems and stochastic signals can provide such a theory and underpin advanced analysis and efficient modelling. It also has the potential to extend beyond these areas to deal with many problems in circuit theory, telecommunications, systems, control theory and signal processing.
Nine international experts have contributed to this work to produce thirteen chapters that can be read independently or as a comprehensive whole with a logical line of reasoning:
Construction and analysis of generalized orthogonal basis function model structure;
System Identification in a time domain setting and related issues of variance, numerics, and uncertainty bounding;
System identification in the frequency domain;
Design issues and optimal basis selection;
Transformation and realization theory.
Modelling and Identification with Rational Orthogonal Basis Functions affords a self-contained description of the development of the field over the last 15 years, furnishing researchers and practising engineers working with dynamical systems and stochastic processes with a standard reference work.
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