Average Reviews:
(More customer reviews)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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This authoritative volume on statistical and adaptive signal processing offers you a unified, comprehensive and practical treatment of spectral estimation, signal modeling, adaptive filtering, and array processing. Packed with over 3,000 equations and more than 300 illustrations, this unique resource provides you with balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional engineers and students alike. From the fundamentals of discrete-time signal processing and linear signal models, to optimum linear filters and least-squares filtering and prediction, you get in-depth information on a broad range of critical topics from leading experts in industry and academia. This invaluable reference provides clear examples, problem sets, and computer experiments that help you master the material and learn how to implement various methods presented in the book. You also find a set of Matlab® functions that illustrate the use of various techniques and can be used to solve real-world problems in the field.
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