Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing (Artech House Signal Processing Library)
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Authors: -
Dimitris G. Manolakis
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Dimitris Manolakis
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Vinay K. Ingle
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Stephen M. Kogon
Description:
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Reviews:
Broad coverage but not focuss.
I buy this book approximately 3 years ago (2000). My goal was to
obtain a better arrangement on the statistical dsp. I obtained the
conference on the dsp avançé on my control. I must admit that for me
it is not prone easy to the Master. Consequently an open space and a
well explained book on this subject are very important for me. But I
was dissapointed when I obtained this book! Yes, it is broad
insurance, but the contents are not focuss. Connection between the
preceding and nearest parts of the discussion and the chapters most of
the time does not show a clear bond. Yes, it is full with the facts,
but I need than volume of facts: MANNER OF the THOUGHT, why we make
and why we do not make. It did not help me to control the subject. For
the reader which wants easy to read and release themselves in the good
explaination as well as the reasoning and examples, I would suggest
going to seek another book such as Steven Mr. Kay (volume 1, theory of
evaluation). For me it is a much better investment of time and money!.
Thank you to read my review.
where is the errata for this book?
I try to study of this book and, because there are some errors, I am
interested where I could find the errata. I tested with the
McGraw-Hill without success. An idea? Thank you.
A good read, especially for an advanced course on DSP
This book gives a short overall picture of the fundamental principles
of the numerical signal Treating and the stochastic methods, before
receiving a diploma with the matters of core, EstimationNon-parametric
evaluation to model namely by signal and parameter, optimal design of
filter and structurs, RLS, LMS and filters adaptive. However high on
the contents, the organization topics of the book starts from much
part for the improvement. A logical order of the matters studied by a
avançé student of level DSP is recommended as follows - 5. Linear
Models Of Signal, 9. To model signal and parametric spectral
evaluation, 6. Optimum Linear Filters, 7. Algorithms and structures
for the optimum linear filters, 10. Adaptive Filters, 8. Filtering and
forecast of least squares, 11. Matric treatment. You can have to
continue to jump of the matters avançées towards the end of a
chapter, to only return later then after having passed by the relative
primary product in other chapters. In this respect, this volume is
indeed inconvenient. However, the authors more than were compensated
for all its defects with the depth of the contents, and also the
width. Supposing that this book is meant for avançé reader, it is
really art of the self-portrait contained, of the ground to the top,
except some details of low level minors, the authors assumed of which
former knowledge. Chapters 11 and 12 treat primarily very specialized
requests engineers and people of radar treating of the maths
esoteric implying of the techniques of treatment of the signals - the
example are the matters on the déconvolution without visibility and
supervised adaptive filtering Non. The authors also provided
rudimentary basic information on the algebra of Holor (algebra of
matrix and vector in particular.) I would recommend the reader to keep
a more fundamental text on mathematical methods for the treatment of
the signals as reference all while using some this book. An example is
mathematical methods and algorithms for the treatment of the signals,
by Todd K Moon and Wynn C Sterling.
avoid this book
This book was the text for the first part of a sequence on statistical signal processing which I took. We covered chapters 1-7, chapters 4-7 in great detail.
This book almost never explains things well. It has a lot of detail and a lot of repetition, but the essence is usually poorly explained. For every important concept, like the Levinson-Durban algorithm or Yule-Walker equations, I had to read other books to figure them out. Stoica has more insight in 2 pages on the Y-W equations then Manolakis has in a dozen. Also the structure and flow of the detailed developments is astonishingly bad. For example, there are algorithms with steps out of order.
Another problem is the huge number of mistakes. It will take you about an hour per chapter to fix each chapter using the publishers errata sheet. But there are errors not included on the errata list, so watch out.
The few good things about this book include a very detailed table of contents, and useful introductory discussions for each chapter and chapter 1.
This book is a disaster and reads like a so-so first draft. Shame on the publisher for not enforcing more quality. Instead of this loser, I recommend Adaptive Filters by Haykin, Spectral Analysis by Stoica, and Mathematical Methods for Signal Processing by Moon and Stirling among others.
a good text book with things to improve
I was likely to read the major part of the text, and attacks some of
the problems selectively. Here my opinions: For 1. Some of the
chapters, including the introduction first those, are very well
written, and pleasant for reading; 2. Many technical contents of
cover; 3. surely echoes the "statistical and adaptive" title. Against
1. some chapters is little a swine, including most important such as
chapter 6 and 10. Well better of the approaches could be employed; 2.
excersizes are not as well projected. Not much of really problems of
innovative/inspiring are given. More various levels of difficulty of
the need. Generally a little easy. the 3. references are not very of
support. If you find a section or discussion interesting, you would
like that the book threads some papers of newspaper of top in this
exit to refer. It did not carry out an exceptional work in this
direction. Above all, a book above of the average text/reference, good
if taught by a good professor. However, the need to introduce into
many perspicacities and better a examples/excersizes to make with the
book a traditional truth. However, against mentioned it above moreover
the text itself gives the impression of people who the authors are or
the Masters not yet true material, or they hurried to the top
finishing the book. But they have much knowledge.
Another great DSP text by Manolakis!
I believe this book is destined to become the "classic" graduate text used to teach statistical and adaptive digital signal processing.If you enjoyed the introductory text "Digital Signal Processing" by Proakis and Manolakis (Prentice-Hall 1996), I think you will enjoy this book by Manolakis, Ingle, and Kogon. It is written in a similar style, with an introduction to each chapter previewing the material to be covered, a logical development of the material including examples, and a conclusion summarizing the high points of the material covered.
At chapter's end, there is a set of well thought out exercises ranging from easy to difficult. There are no answers to the problems in the back of the book, but there are enough examples in each chapter that one should be able to tackle most of the exercises. Some of the exercises require MATLAB. The authors have written some custom MATLAB functions which are available from the publisher as an e-Mail attachment.
I would say this book is written at the graduate level and requires knowledge of several disciplines: 1) DSP- At the level of Proakis + Manolakis intro text (cited above). 2) Linear Algebra- Cramer's rule, LDU factorization, eigenvalues, eigenvectors, Hermitian and Unitary matrices, etc. 3) Statistics- Random variables, averages, variances, estimators, sampling distributions, auto- and cross- correlations. I had no previous knowledge of stochastic processes, and was able to pick up enough from Chapter 3 to get through the rest of the book.
This book is, above all, a mathematical text written for engineers. It describes the theory and equations underlying statistical filters.
There is a lot of meat in each section. I typically had to read each section an average of 3 times for it to sink in.
With help from Figure 1.2.8 of the book, it covers the following material:
Chapter 1- Introduction to applications of spectral estimation, signal modeling, adaptive filtering, and array processing.
Chapter 2- Review of discrete-time signal processing.
Chapter 3- Review of random vectors and signals: properties, linear transformations, and estimation.
Chapter 4- Random signal models with rational system functions (AR, MA, ARMA, ARIMA).
Chapter 5- Nonparametric spectral estimation.
Chapter 6- Optimum filters and predictors -- matched filters (including Wiener) and eigenfilters.
Chapter 7- Algorithms and structures for optimum filtering (including algorithms of Levinson, Levinson-Durbin, Schur, Kalman, ...)
Chapter 8- Least-squares filtering and prediction (normal equations, orthogonalization, SVD).
Chapter 9- Signal modeling and parametric spectrum estimation.
Chapter 10- Adaptive filters: Design, performance, implementation, and applications (includes steepest descent, LMS, NLMS, CRLS, QR-RLS, fast RLS, fast Kalman, RLS lattice-ladder, ...).
Chapter 11- Array processing: theory, algorithms, and applications.
Chapter 12- Higher order statistics, blind deconvolution and equalization, fractional and fractal random signal models.
Appendix B includes the clearest, most graphic example of LaGrange multipliers I have ever seen!
Note that this book deliberately leaves out the following topics because it is NOT meant to be a text that covers ALL of ADVANCED DSP: Multirate DSP, Wavelets, etc.
I highly recommend this book to anyone involved in spectral estimation, signal modeling, adaptive filtering, or array processing.
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