Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory Author: Steven Kay | Language: English | ISBN:
0133457117 | Format: PDF
Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory Description
A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous examples as well as applications to real- world problems. MARKETS: For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals — radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.
- Hardcover: 625 pages
- Publisher: Prentice Hall; 1 edition (April 5, 1993)
- Language: English
- ISBN-10: 0133457117
- ISBN-13: 978-0133457117
- Product Dimensions: 9.5 x 7.2 x 1.1 inches
- Shipping Weight: 2.3 pounds (View shipping rates and policies)
In this book, Steven M. Kay has produced an excellent tutorial and research reference book on estimation theory. The book covers enough introductory material for someone with a reasonable undergraduate understanding of statistics to pick up the ideas quickly. The theory is illustrated with very concrete examples; the examples give an "under-the-hood" insight into the solution of some common estimation problems in signal processing. If you're a statistician, you might not like this book. If you're an engineer, you will like it.
By Peter J. Kootsookos
Without any hesitation, I consider this book as a masterpiece in the area of statistical signal processing. Kay takes the reader to the journey of estimation theory as if a science teacher takes his students to a field trip. The one special feature of this book is the convergence of thought that reader obtains upon reading the book. Kay lays a fundamental bridge between various estimators using his succinct style for describing the subject.
Few special areas require more attention in this book. For example the coverage of EM methods is very condense and requires more elaboration. Also there is no discussion on the estimation methods using higher order statistics.
Overall I consider this book as the best book I have read ever and I highly recommend this book to those who want to obtain an ever-lasting view on statistical signal processing.
By Navid Lashkarian
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