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Author : Peter J. Schreier
Publisher : Unknown
Page : 331 pages
File Size : 47,5 Mb
Release : 2010
Category : Electronic
ISBN : 0511678975
Author : Peter J. Schreier,Louis L. Scharf
Publisher : Cambridge University Press
Page : 331 pages
File Size : 47,5 Mb
Release : 2010-02-04
Category : Technology & Engineering
ISBN : 9781139487627
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.
Author : Eric Moreau,Tülay Adali
Publisher : John Wiley & Sons
Page : 112 pages
File Size : 49,8 Mb
Release : 2013-10-07
Category : Technology & Engineering
ISBN : 9781848214590
Blind identification consists of estimating a multi-dimensional system only through the use of its output, and source separation, the blind estimation of the inverse of the system. Estimation is generally carried out using different statistics of the output. The authors of this book consider the blind identification and source separation problem in the complex-domain, where the available statistical properties are richer and include non-circularity of the sources – underlying components. They define identifiability conditions and present state-of-the-art algorithms that are based on algebraic methods as well as iterative algorithms based on maximum likelihood theory. Contents 1. Mathematical Preliminaries. 2. Estimation by Joint Diagonalization. 3. Maximum Likelihood ICA. About the Authors Eric Moreau is Professor of Electrical Engineering at the University of Toulon, France. His research interests concern statistical signal processing, high order statistics and matrix/tensor decompositions with applications to data analysis, telecommunications and radar. Tülay Adali is Professor of Electrical Engineering and Director of the Machine Learning for Signal Processing Laboratory at the University of Maryland, Baltimore County, USA. Her research interests concern statistical and adaptive signal processing, with an emphasis on nonlinear and complex-valued signal processing, and applications in biomedical data analysis and communications. Blind identification consists of estimating a multidimensional system through the use of only its output. Source separation is concerned with the blind estimation of the inverse of the system. The estimation is generally performed by using different statistics of the outputs. The authors consider the blind estimation of a multiple input/multiple output (MIMO) system that mixes a number of underlying signals of interest called sources. They also consider the case of direct estimation of the inverse system for the purpose of source separation. They then describe the estimation theory associated with the identifiability conditions and dedicated algebraic algorithms. The algorithms depend critically on (statistical and/or time frequency) properties of complex sources that will be precisely described.
Author : Louis L. Scharf,Cédric Demeure
Publisher : Prentice Hall
Page : 552 pages
File Size : 53,8 Mb
Release : 1991
Category : Technology & Engineering
ISBN : UOM:39015048228186
This book embraces the many mathematical procedures that engineers and statisticians use to draw inference from imperfect or incomplete measurements. This book presents the fundamental ideas in statistical signal processing along four distinct lines: mathematical and statistical preliminaries; decision theory; estimation theory; and time series analysis.
Author : Debasis Kundu,Swagata Nandi
Publisher : Springer Science & Business Media
Page : 132 pages
File Size : 47,8 Mb
Release : 2012-05-24
Category : Computers
ISBN : 9788132206286
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.
Author : Robert M. Gray,Lee D. Davisson
Publisher : Cambridge University Press
Page : 479 pages
File Size : 55,5 Mb
Release : 2004-12-02
Category : Technology & Engineering
ISBN : 9781139456289
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.
Author : Richard Shiavi
Publisher : Elsevier
Page : 424 pages
File Size : 53,7 Mb
Release : 2010-07-19
Category : Technology & Engineering
ISBN : 9780080467689
Introduction to Applied Statistical Signal Analysis, Third Edition, is designed for the experienced individual with a basic background in mathematics, science, and computer. With this predisposed knowledge, the reader will coast through the practical introduction and move on to signal analysis techniques, commonly used in a broad range of engineering areas such as biomedical engineering, communications, geophysics, and speech. Topics presented include mathematical bases, requirements for estimation, and detailed quantitative examples for implementing techniques for classical signal analysis. This book includes over one hundred worked problems and real world applications. Many of the examples and exercises use measured signals, most of which are from the biomedical domain. The presentation style is designed for the upper level undergraduate or graduate student who needs a theoretical introduction to the basic principles of statistical modeling and the knowledge to implement them practically. Includes over one hundred worked problems and real world applications. Many of the examples and exercises in the book use measured signals, many from the biomedical domain.
Author : Abdelhak M. Zoubir,D. Robert Iskander
Publisher : Cambridge University Press
Page : 238 pages
File Size : 43,6 Mb
Release : 2004-05-06
Category : Technology & Engineering
ISBN : 1139452029
The statistical bootstrap is one of the methods that can be used to calculate estimates of a certain number of unknown parameters of a random process or a signal observed in noise, based on a random sample. Such situations are common in signal processing and the bootstrap is especially useful when only a small sample is available or an analytical analysis is too cumbersome or even impossible. This book covers the foundations of the bootstrap, its properties, its strengths and its limitations. The authors focus on bootstrap signal detection in Gaussian and non-Gaussian interference as well as bootstrap model selection. The theory developed in the book is supported by a number of useful practical examples written in MATLAB. The book is aimed at graduate students and engineers, and includes applications to real-world problems in areas such as radar and sonar, biomedical engineering and automotive engineering.
Author : Danilo P. Mandic,Vanessa Su Lee Goh
Publisher : John Wiley & Sons
Page : 344 pages
File Size : 41,5 Mb
Release : 2009-04-20
Category : Science
ISBN : 9780470742631
This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals.
Author : Are Hjørungnes
Publisher : Cambridge University Press
Page : 271 pages
File Size : 49,7 Mb
Release : 2011-02-24
Category : Technology & Engineering
ISBN : 9781139498043
In this complete introduction to the theory of finding derivatives of scalar-, vector- and matrix-valued functions with respect to complex matrix variables, Hjørungnes describes an essential set of mathematical tools for solving research problems where unknown parameters are contained in complex-valued matrices. The first book examining complex-valued matrix derivatives from an engineering perspective, it uses numerous practical examples from signal processing and communications to demonstrate how these tools can be used to analyze and optimize the performance of engineering systems. Covering un-patterned and certain patterned matrices, this self-contained and easy-to-follow reference deals with applications in a range of areas including wireless communications, control theory, adaptive filtering, resource management and digital signal processing. Over 80 end-of-chapter exercises are provided, with a complete solutions manual available online.
Author : Umberto Spagnolini
Publisher : John Wiley & Sons
Page : 604 pages
File Size : 40,8 Mb
Release : 2018-02-05
Category : Technology & Engineering
ISBN : 9781119293972
A problem-solving approach to statistical signal processing for practicing engineers, technicians, and graduate students This book takes a pragmatic approach in solving a set of common problems engineers and technicians encounter when processing signals. In writing it, the author drew on his vast theoretical and practical experience in the field to provide a quick-solution manual for technicians and engineers, offering field-tested solutions to most problems engineers can encounter. At the same time, the book delineates the basic concepts and applied mathematics underlying each solution so that readers can go deeper into the theory to gain a better idea of the solution’s limitations and potential pitfalls, and thus tailor the best solution for the specific engineering application. Uniquely, Statistical Signal Processing in Engineering can also function as a textbook for engineering graduates and post-graduates. Dr. Spagnolini, who has had a quarter of a century of experience teaching graduate-level courses in digital and statistical signal processing methods, provides a detailed axiomatic presentation of the conceptual and mathematical foundations of statistical signal processing that will challenge students’ analytical skills and motivate them to develop new applications on their own, or better understand the motivation underlining the existing solutions. Throughout the book, some real-world examples demonstrate how powerful a tool statistical signal processing is in practice across a wide range of applications. Takes an interdisciplinary approach, integrating basic concepts and tools for statistical signal processing Informed by its author’s vast experience as both a practitioner and teacher Offers a hands-on approach to solving problems in statistical signal processing Covers a broad range of applications, including communication systems, machine learning, wavefield and array processing, remote sensing, image filtering and distributed computations Features numerous real-world examples from a wide range of applications showing the mathematical concepts involved in practice Includes MATLAB code of many of the experiments in the book Statistical Signal Processing in Engineering is an indispensable working resource for electrical engineers, especially those working in the information and communication technology (ICT) industry. It is also an ideal text for engineering students at large, applied mathematics post-graduates and advanced undergraduates in electrical engineering, applied statistics, and pure mathematics, studying statistical signal processing.
Author : Abdelhak M. Zoubir,Visa Koivunen,Esa Ollila,Michael Muma
Publisher : Cambridge University Press
Page : 315 pages
File Size : 44,9 Mb
Release : 2018-11-08
Category : Mathematics
ISBN : 9781107017412
Understand the benefits of robust statistics for signal processing using this unique and authoritative text.
Author : John G. Proakis
Publisher : Unknown
Page : 584 pages
File Size : 42,5 Mb
Release : 2002
Category : Computers
ISBN : UOM:39015053184167
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.
Author : Saeid Sanei,Hossein Hassani
Publisher : CRC Press
Page : 260 pages
File Size : 50,9 Mb
Release : 2015-12-23
Category : Medical
ISBN : 9781466589285
Recent advancements in signal processing and computerised methods are expected to underpin the future progress of biomedical research and technology, particularly in measuring and assessing signals and images from the human body. This book focuses on singular spectrum analysis (SSA), an effective approach for single channel signal analysis, and its bivariate, multivariate, tensor based, complex-valued, quaternion-valued and robust variants. SSA currently has numerous applications in detecting abnormalities in quasi-periodic biosignals, such as electrocardiograms, (ECGs or EKGs), oxygen levels, arterial pressure, and electroencephalograms (EEGs). Singular Spectrum Analysis of Biomedical Signals presents relatively newly applied concepts for biomedical applications of SSA, including: Signal source separation, extraction, decomposition, and factorization Physiological, biological, and biochemical signal processing A new SSA grouping algorithm for filtering and noise reduction of genetics data Prediction of various clinical events The book introduces a new mathematical and signal processing technique for the decomposition of widely available single channel biomedical data. It also provides illustrations of new signal processing results in the form of signals, graphs, images, and tables to reinforce understanding of the related concepts. Singular Spectrum Analysis of Biomedical Signals enhances current clinical knowledge and aids physicians in improving diagnosis, treatment and monitoring some clinical abnormalities. It also lays groundwork for progress in SSA by making suggestions for future research.
Author : Antonio Napolitano
Publisher : John Wiley & Sons
Page : 504 pages
File Size : 54,8 Mb
Release : 2012-12-07
Category : Technology & Engineering
ISBN : 9781118437919
The relative motion between the transmitter and the receivermodifies the nonstationarity properties of the transmitted signal.In particular, the almost-cyclostationarity property exhibited byalmost all modulated signals adopted in communications, radar,sonar, and telemetry can be transformed into more general kinds ofnonstationarity. A proper statistical characterization of thereceived signal allows for the design of signal processingalgorithms for detection, estimation, and classification thatsignificantly outperform algorithms based on classical descriptionsof signals.Generalizations of Cyclostationary SignalProcessing addresses these issues and includes thefollowing key features: Presents the underlying theoretical framework, accompanied bydetails of their practical application, for the mathematical modelsof generalized almost-cyclostationary processes and spectrallycorrelated processes; two classes of signals finding growingimportance in areas such as mobile communications, radar andsonar. Explains second- and higher-order characterization ofnonstationary stochastic processes in time and frequencydomains. Discusses continuous- and discrete-time estimators ofstatistical functions of generalized almost-cyclostationaryprocesses and spectrally correlated processes. Provides analysis of mean-square consistency and asymptoticNormality of statistical function estimators. Offers extensive analysis of Doppler channels owing to therelative motion between transmitter and receiver and/or surroundingscatterers. Performs signal analysis using both the classicalstochastic-process approach and the functional approach, wherestatistical functions are built starting from a single function oftime.