Random Signals, Noise and Filtering develops the theory of random processes and its application to the study of systems and analysis of random data. The text covers three important areas: (1) fundamentals and examples of random process models, (2) applications of probabilistic models: signal detection, and filtering, and (3) statistical estimation--measurement and analysis of random data to determine the structure and parameter values of probabilistic models. This volume by Breipohl and Shanmugan offers the only one-volume treatment of the fundamentals of random process models, their applications, and data analysis.
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Random Signals, Noise and Filtering develops the theory of random processes and its application to the study of systems and analysis of random data. The text covers three important areas: (1) fundamentals and examples of random process models, (2) applications of probabilistic models: signal detection, and filtering, and (3) statistical estimation--measurement and analysis of random data to determine the structure and parameter values of probabilistic models. This volume by Breipohl and Shanmugan offers the only one-volume treatment of the fundamentals of random process models, their applications, and data analysis.
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Most Helpful Customer Reviews
6 of 6 people found the following review helpful:
5.0 out of 5 stars
Excellent Book !,
By Master of Puppets "MoP" (Lawrence, KS United States) - See all my reviews
This review is from: Random Signals: Detection, Estimation and Data Analysis (Paperback)
This is one of the best books around for studying Random Processes ! The author has also provided a very good introduction to Detection, Estimation and Modelling of Stochastic Processes. I found this book very useful and I'd strongly suggest this book for an introductionary level graduate course. If you want to build a strong foundation in Random Signal Theory, this book is the way to go.. Other advanced texts like Simon Haykin's "Adaptive Filter Theory" will be a lot easier to understand once you study this book thoroughly and work out the exercise problems.
2 of 2 people found the following review helpful:
5.0 out of 5 stars
An excellent book,
By
This review is from: Random Signals: Detection, Estimation and Data Analysis (Paperback)
I've read the statistical signal processing and stochastic processes books by Kay, Papoulis, Srinath and Stark & Woods, and this is by far the best book that covers both subject areas in a logical fashion. The text is very clearly written, mathematical notation is easy to follow, and example problems are very worthwhile. I'm currently a PhD student using this book to prepare for my qualification exams, and it's really helping me master the difficult subject of estimation and detection theory.
Though the book does not get much into measure spaces and some of the abstract theoretical fundamentals, it's an excellent engineering reference that's ideal for an introductory class in the subject. My only complaint is that the book is not hardcover.
2 of 2 people found the following review helpful:
5.0 out of 5 stars
Much better than Hayes,
By S. K. (Israel) - See all my reviews
This review is from: Random Signals: Detection, Estimation and Data Analysis (Paperback)
I agree with the other reviews that this is one of the best books for studying random processes, especially in the context of DSP. I used the book in a graduate level "statistical signal processing" course at the University of London and I found it extremely useful.
It covers everything from the definition of a sample space, AR and MA processes, periodograms to optimal Wiener filter theory. The examples are very clear and they accompany each of the chapters. One point to notice is that you do need to know something about Fourier transforms and also have basic familiarity with probability. Highly recommended to anyone in the DSP field.
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Key Phrases - Statistically Improbable Phrases (SIPs):
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optimum realizable filter, optimum unrealizable filter, random binary waveform, find rxx, single member function, composite alternative hypothesis, spectrum factorization, binary detection problem, periodogram estimator, binary communication system, random process models, partial autocorrelation coefficients, power spectral density function, quadrature representation, joint probability mass function, rms bandwidth, analog communication systems, uncorrelated samples, received waveform, random experiment, unity variance, optimum filter, autocorrelation function, spectral density functions, joint pdf Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, John Wiley, Stochastic Processes, Monte Carlo, San Francisco, Transmitter Transmission, Substituting Equation, Engineering Sciences, Englewood Cliffs, Solve Equations New!
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optimum realizable filter, optimum unrealizable filter, random binary waveform, find rxx, single member function, composite alternative hypothesis, spectrum factorization, binary detection problem, periodogram estimator, binary communication system, random process models, partial autocorrelation coefficients, power spectral density function, quadrature representation, joint probability mass function, rms bandwidth, analog communication systems, uncorrelated samples, received waveform, random experiment, unity variance, optimum filter, autocorrelation function, spectral density functions, joint pdf Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, John Wiley, Stochastic Processes, Monte Carlo, San Francisco, Transmitter Transmission, Substituting Equation, Engineering Sciences, Englewood Cliffs, Solve Equations New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 10
books:
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16
books
cite this book:
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- Advanced Theory of Statistics, Vol. 3 by M. G. Kendall on page 97
- Kalman Filtering Theory (University Series in Modern Engineering) by A. V. Balakrishnan on page 467
- Digital and Kalman Filtering, Second Edition by S. Bozic on page 467
- Probability and Stochastic Processes for Engineers by Carl W. Helstrom on page 96
- Signals and Systems: Analysis of Signals Through Linear Systems by M.J. Roberts on page 243
See all 10 books this book cites
- Signal Detection and Estimation [With CDROM] (Artech House Radar Library) by Mourad Barkat on page 140, page 287, and page 288
- Introduction to Applied Statistical Signal Analysis, Second Edition (Biomedical Engineering) by Richard Shiavi on page 142, and page 188
- Trellis and Turbo Coding by Christian Schlegel on page 50
- Signal Processing Noise (Electrical Engineering & Applied Signal Processing Series) by Vyacheslav Tuzlukov on page 245
- Advanced Signal Processing and Noise Reduction, 2nd Edition by Professor Saeed V. Vaseghi on page 88
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