This book is intended as a textbook for graduate students who are assumed to have taken some basic signal processing courses, or as a practicing researcher's reference. The topic is well presented, in that the different processing schemes are derived clearly and consicely. Indeed, it is a formidable achievement for an author to cover so many processing schemes so clearly and with such detail within some 450 pages. On the parts of the contents, this book is a potential standard desktop reference for the researcher or engineer working in the field of signal processing.

Unfortunately, the book itself does not quite meet the standards of its contents. The layout and typography leaves, I think, much to be desired. By necessity, such a text is heavily boggled with math, but the word processor used to typeset the text has not been able to generate a very pleasant typeface, resulting in a generally messy appearance of the text. The contents of some chapters of this book is precicely on the point of my work. However, I chose to use another book, that was slightly off topic, but which was a more pleasant read due to typography.

This book is, by contents, a potential standard reference for the signal processing practitioner and as such clearly worth the five star score. However, the reader who appreciates the visual appearance of a text, may want to pick up a copy in the local bookstore and judge for himself if the contents makes up for the messy typeface.

Primarily based on a good set of topics in the table of contents, I chose this book in an earlier semester as the text for a graduate level course in spectral analysis. The assumed background for our students is basic experience with Fourier transforms, general statistics, and Fortran programming. The students found this book almost useless, though, because it is at too high a level for a first course. I did not find it nearly as useful as the classic by Jenkins and Watts "Spectral Analysis and its Applications" or the first edition of the book by Peter Bloomfield, "Fourier Analysis of Time Series." (I haven't seen the second edition of Bloomfield yet.) In my opinion, Naidu's book is better for people who already have experience with time series analysis and who want to supplement their knowledge. There is a good list of references at the end of each chapter, and a number of topics go beyond "classical" techniques, as you would expect from the book's title.