Modern Spectrum Analysis PDF Download

Author: Donald G. Childers
Publisher: IEEE Computer Society Press
ISBN:
Category : Science
Languages : en
Pages : 348

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Book Description

Author: Donald G. Childers
Publisher:
ISBN:
Category :
Languages : en
Pages : 334

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Book Description

Author: Prabhakar S. Naidu
Publisher: CRC Press
ISBN: 9780849324642
Category : Mathematics
Languages : en
Pages : 424

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Book Description
Spectrum analysis can be considered as a topic in statistics as well as a topic in digital signal processing (DSP). This book takes a middle course by emphasizing the time series models and their impact on spectrum analysis. The text begins with elements of probability theory and goes on to introduce the theory of stationary stochastic processes. The depth of coverage is extensive. Many topics of concern to spectral characterization of Gaussian and non-Gaussian time series, scalar and vector time series are covered. A section is devoted to the emerging areas of non-stationary and cyclostationary time series. The book is organized more as a textbook than a reference book. Each chapter includes many examples to illustrate the concepts described. Several exercises are included at the end of each chapter. The level is appropriate for graduate and research students.

Author: Stanislav B. Kesler
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
ISBN:
Category : Science
Languages : en
Pages : 464

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Book Description

Author: Petre Stoica
Publisher: Pearson Education
ISBN:
Category : Mathematics
Languages : en
Pages : 358

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Book Description
This book presents an introduction to spectral analysis that is designed for either course use or self-study. Clear and concise in approach, it develops a firm understanding of tools and techniques as well as a solid background for performing research. Topics covered include nonparametric spectrum analysis (both periodogram-based approaches and filter- bank approaches), parametric spectral analysis using rational spectral models (AR, MA, and ARMA models), parametric method for line spectra, and spatial (array) signal processing. Analytical and Matlab-based computer exercises are included to develop both analytical skills and hands-on experience.

Author: Morris Engelson
Publisher:
ISBN:
Category : Science
Languages : en
Pages : 284

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Book Description

Author: Donald B. Percival
Publisher: Cambridge University Press
ISBN: 9780521435413
Category : Mathematics
Languages : en
Pages : 616

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Book Description
This book is an up-to-date introduction to univariate spectral analysis at the graduate level, which reflects a new scientific awareness of spectral complexity, as well as the widespread use of spectral analysis on digital computers with considerable computational power. The text provides theoretical and computational guidance on the available techniques, emphasizing those that work in practice. Spectral analysis finds extensive application in the analysis of data arising in many of the physical sciences, ranging from electrical engineering and physics to geophysics and oceanography. A valuable feature of the text is that many examples are given showing the application of spectral analysis to real data sets. Special emphasis is placed on the multitaper technique, because of its practical success in handling spectra with intricate structure, and its power to handle data with or without spectral lines. The text contains a large number of exercises, together with an extensive bibliography.

Author: Markus Båth
Publisher:
ISBN:
Category : Geophysics
Languages : en
Pages : 544

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Book Description

Author: Steven M. Kay
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 574

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Book Description

Author: Yanwei Wang
Publisher: Morgan & Claypool Publishers
ISBN: 1598290002
Category : Computers
Languages : en
Pages : 108

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Book Description
Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.