An Introduction to Statistical Signal Processing
[book]bName=An Introduction to Statistical Signal Processing;bAuthor=Robert M. Gray;bISBN=7-04-01330;bPub=Cambridge University Press;bVer=2004/1;bLang=en;bFormat=pdf;bCover=http://bbs.itebook.net/book.gif[/book]The origins of this book lie in our earlier book Random Processes: A Math-
ematical Approach for Engineers (Prentice Hall, 1986). This book began as
a second edition to the earlier book and the basic goal remains unchanged
– to introduce the fundamental ideas and mechanics of random processes to
engineers in a way that accurately reflects the underlying mathematics, but
does not require an extensive mathematical background and does not belabor
detailed general proofs when simple cases suffice to get the basic ideas
across. In the years since the original book was published, however, it has
evolved into something bearing little resemblance to its ancestor. Numerous
improvements in the presentation of the material have been suggested
by colleagues, students, teaching assistants, and reviewers, and by our own
teaching experience. The emphasis of the book shifted increasingly towards
examples and a viewpoint that better reflected the title of the courses we
taught using the book for many years at Stanford University and at the
University of Maryland: An Introduction to Statistical Signal Processing.
Much of the basic content of this course and of the fundamentals of random
processes can be viewed as the analysis of statistical signal processing systems:
typically one is given a probabilistic description for one random object,
which can be considered as an input signal. An operation is applied to the
input signal (signal processing) to produce a new random object, the output
signal. Fundamental issues include the nature of the basic probabilistic description,
and the derivation of the probabilistic description of the output
signal given that of the input signal and the particular operation performed.
A perusal of the literature in statistical signal processing, communications,
control, image and video processing, speech and audio processing, medical
signal processing, geophysical signal processing, and classical statistical
areas of time series analysis, classification and regression, and pattern recognition
shows a wide variety of probabilistic models for input processes and
for operations on those processes, where the operations might be deterministic
or random, natural or artificial, linear or nonlinear, digital or analog, or
beneficial or harmful. An introductory course focuses on the fundamentals
underlying the analysis of such systems: the theories of probability, random
processes, systems, and signal processing. |