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Random Signals: Detection, Estimation and Data Analysis, by K. Sam Shanmugan, Arthur M. Breipohl
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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.
- Sales Rank: #1133125 in Books
- Published on: 1988-07
- Original language: English
- Number of items: 1
- Dimensions: 9.57" h x 1.43" w x 6.40" l, 2.04 pounds
- Binding: Paperback
- 688 pages
From the Publisher
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.
Most helpful customer reviews
6 of 6 people found the following review helpful.
Excellent Book !
By Master of Puppets
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.
3 of 3 people found the following review helpful.
An excellent book
By D. Bradley
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.
3 of 3 people found the following review helpful.
Much better than Hayes
By S. K.
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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