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.
Fundamentals of Statistical Processing, Volume I: Estimation Theory is 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.
The outgrowth of a one-semester graduate level course on estimation theory given at the U. of Rhode Island, this text strikes a balance between the highly theoretical expositions written by statisticians and the practical treatments contributed by the many users of applied statistics. The primary focus is on obtaining optimal estimation algorithms that may be implemented on a digital computer. The background assumed is exposure to the basic theory of digital signal processing, probability and random processes, and linear and matrix algebra. Annotation c. Book News, Inc., Portland, OR (booknews.com)