Higher Order Statistical Signal Processing

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Higher Order Statistical Signal Processing

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Author : Boualem Boashash
language : en
Publisher: *Halsted Press
Release Date : 1995

Higher Order Statistical Signal Processing written by Boualem Boashash and has been published by *Halsted Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Technology & Engineering categories.

Higher-Order Statistical Signal Processing brings together some most recent innovations in the field of higher-order statistical signal processing. It is structured to provide a comprehensive understanding of the fundamentals of the discipline, as well as a treatment of recent advances.

Proceedings Of The Ieee Signal Processing Workshop On Higher Order Statistics June 14 16 1999 Caesarea Israel

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Author :
language : en
Publisher: IEEE
Release Date : 1999

Proceedings Of The Ieee Signal Processing Workshop On Higher Order Statistics June 14 16 1999 Caesarea Israel written by and has been published by IEEE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Mathematics categories.

Contains papers from a June 1999 workshop, covering theories, techniques, implementations, and applications of statistical signal processing, with particular emphasis on methods involving the use of higher order statistics (HOS). Papers represent the latest advances in areas of signal processing for communications, convolutive mixtures, HOS-based signal processing theory and methods, heavy-tailed models and processing, Bayesian methods of signal processing, non-stationary signal processing, and HOS-signal processing applications. Specific subjects include higher-order statistical models of visual images, cumulant matrix subspace algorithms for blind single FIR channel identification, and Bayesian wavelet denoising using Besov priors. Lacks a subject index. Annotation copyrighted by Book News, Inc., Portland, OR

Algorithms For Statistical Signal Processing

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Author : John G. Proakis
language : en
Publisher:
Release Date : 2002

Algorithms For Statistical Signal Processing written by John G. Proakis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.

Keeping pace with the expanding, ever more complex applications of DSP, this authoritative presentation of computational algorithms for statistical signal processing focuses on "advanced topics" ignored by other books on the subject. Algorithms for Convolution and DFT. Linear Prediction and Optimum Linear Filters. Least-Squares Methods for System Modeling and Filter Design. Adaptive Filters. Recursive Least-Squares Algorithms for Array Signal Processing. QRD-Based Fast Adaptive Filter Algorithms. Power Spectrum Estimation. Signal Analysis with Higher-Order Spectra. For Electrical Engineers, Computer Engineers, Computer Scientists, and Applied Mathematicians.

Blind Estimation Using Higher Order Statistics

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Author : Asoke Nandi
language : en
Publisher: Springer Science & Business Media
Release Date : 1999-01-31

Blind Estimation Using Higher Order Statistics written by Asoke Nandi and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-01-31 with Technology & Engineering categories.

In the signal-processing research community, a great deal of progress in higher-order statistics (HOS) began in the mid-1980s. These last fifteen years have witnessed a large number of theoretical developments as well as real applications. Blind Estimation Using Higher-Order Statistics focuses on the blind estimation area and records some of the major developments in this field. Blind Estimation Using Higher-Order Statistics is a welcome addition to the few books on the subject of HOS and is the first major publication devoted to covering blind estimation using HOS. The book provides the reader with an introduction to HOS and goes on to illustrate its use in blind signal equalisation (which has many applications including (mobile) communications), blind system identification, and blind sources separation (a generic problem in signal processing with many applications including radar, sonar and communications). There is also a chapter devoted to robust cumulant estimation, an important problem where HOS results have been encouraging. Blind Estimation Using Higher-Order Statistics is an invaluable reference for researchers, professionals and graduate students working in signal processing and related areas.

Higher Order Spectra Analysis

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Author : Chrysostomos L. Nikias
language : en
Publisher: Prentice Hall
Release Date : 1993

Higher Order Spectra Analysis written by Chrysostomos L. Nikias and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Science categories.

This manual will be valuable to practicing engineers who need an introduction to polyspectra from a signal processing perspective. In response to the recent growth of interest in polyspectra, this timely text provides an introduction to signal processing methods that are based on polyspectra and cumulants concepts. The emphasis of the book is placed on the presentation of signal processing tools for use in situations where the more common power spectrum estimation techniques fall short.

Proceedings Of The Ieee Signal Processing Workshop On Higher Order Statistics July 21 23 1997 Banff Alberta Canada

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Author :
language : en
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
Release Date : 1997

Proceedings Of The Ieee Signal Processing Workshop On Higher Order Statistics July 21 23 1997 Banff Alberta Canada written by and has been published by Institute of Electrical & Electronics Engineers(IEEE) this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Computers categories.

This text covering the 1997 IEEE Signal Processing Workshop on High-Order Statistics is designed for researchers, professors, practitioners, students and other computing professionals.

On Statistical Pattern Recognition In Independent Component Analysis Mixture Modelling

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Author : Addisson Salazar
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-07-20

On Statistical Pattern Recognition In Independent Component Analysis Mixture Modelling written by Addisson Salazar and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-20 with Technology & Engineering categories.

A natural evolution of statistical signal processing, in connection with the progressive increase in computational power, has been exploiting higher-order information. Thus, high-order spectral analysis and nonlinear adaptive filtering have received the attention of many researchers. One of the most successful techniques for non-linear processing of data with complex non-Gaussian distributions is the independent component analysis mixture modelling (ICAMM). This thesis defines a novel formalism for pattern recognition and classification based on ICAMM, which unifies a certain number of pattern recognition tasks allowing generalization. The versatile and powerful framework developed in this work can deal with data obtained from quite different areas, such as image processing, impact-echo testing, cultural heritage, hypnograms analysis, web-mining and might therefore be employed to solve many different real-world problems.

Generalizations Of Cyclostationary Signal Processing

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Author : Antonio Napolitano
language : en
Publisher: John Wiley & Sons
Release Date : 2012-12-07

Generalizations Of Cyclostationary Signal Processing written by Antonio Napolitano and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-07 with Technology & Engineering categories.

The relative motion between the transmitter and the receiver modifies the nonstationarity properties of the transmitted signal. In particular, the almost-cyclostationarity property exhibited by almost all modulated signals adopted in communications, radar, sonar, and telemetry can be transformed into more general kinds of nonstationarity. A proper statistical characterization of the received signal allows for the design of signal processing algorithms for detection, estimation, and classification that significantly outperform algorithms based on classical descriptions of signals.Generalizations of Cyclostationary Signal Processing addresses these issues and includes the following key features: Presents the underlying theoretical framework, accompanied by details of their practical application, for the mathematical models of generalized almost-cyclostationary processes and spectrally correlated processes; two classes of signals finding growing importance in areas such as mobile communications, radar and sonar. Explains second- and higher-order characterization of nonstationary stochastic processes in time and frequency domains. Discusses continuous- and discrete-time estimators of statistical functions of generalized almost-cyclostationary processes and spectrally correlated processes. Provides analysis of mean-square consistency and asymptotic Normality of statistical function estimators. Offers extensive analysis of Doppler channels owing to the relative motion between transmitter and receiver and/or surrounding scatterers. Performs signal analysis using both the classical stochastic-process approach and the functional approach, where statistical functions are built starting from a single function of time.

An Introduction To Statistical Signal Processing

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Author : Robert M. Gray
language : en
Publisher: Cambridge University Press
Release Date : 2004-12-02

An Introduction To Statistical Signal Processing written by Robert M. Gray and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-12-02 with Technology & Engineering categories.

This book describes the essential tools and techniques of statistical signal processing. At every stage theoretical ideas are linked to specific applications in communications and signal processing using a range of carefully chosen examples. The book begins with a development of basic probability, random objects, expectation, and second order moment theory followed by a wide variety of examples of the most popular random process models and their basic uses and properties. Specific applications to the analysis of random signals and systems for communicating, estimating, detecting, modulating, and other processing of signals are interspersed throughout the book. Hundreds of homework problems are included and the book is ideal for graduate students of electrical engineering and applied mathematics. It is also a useful reference for researchers in signal processing and communications.

Statistical Signal Processing

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Author : Debasis Kundu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-05-24

Statistical Signal Processing written by Debasis Kundu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-24 with Computers categories.

Signal processing may broadly be considered to involve the recovery of information from physical observations. The received signal is usually disturbed by thermal, electrical, atmospheric or intentional interferences. Due to the random nature of the signal, statistical techniques play an important role in analyzing the signal. Statistics is also used in the formulation of the appropriate models to describe the behavior of the system, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Statistical signal processing basically refers to the analysis of random signals using appropriate statistical techniques. The main aim of this book is to introduce different signal processing models which have been used in analyzing periodic data, and different statistical and computational issues involved in solving them. We discuss in detail the sinusoidal frequency model which has been used extensively in analyzing periodic data occuring in various fields. We have tried to introduce different associated models and higher dimensional statistical signal processing models which have been further discussed in the literature. Different real data sets have been analyzed to illustrate how different models can be used in practice. Several open problems have been indicated for future research.