Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory
Author: Kay
Publisher: Pearson Education India
Published:
Total Pages: 612
ISBN-13: 9788131728994
DOWNLOAD EBOOK →Lanterna Rally
eBook PDF Download Digital Library
Fundamentals of Statistical Signal Processing
Author: Steven M. Kay
Publisher: Pearson Education
Published: 2013
Total Pages: 496
ISBN-13: 013280803X
DOWNLOAD EBOOK →"For those involved in the design and implementation of signal processing algorithms, this book strikes a balance between highly theoretical expositions and the more practical treatments, covering only those approaches necessary for obtaining an optimal estimator and analyzing its performance. Author Steven M. Kay discusses classical estimation followed by Bayesian estimation, and illustrates the theory with numerous pedagogical and real-world examples."--Cover, volume 1.
Fundamentals of Statistical Signal Processing
Author: Steven M. Kay
Publisher: Prentice Hall
Published: 1993
Total Pages: 595
ISBN-13: 9780130422682
DOWNLOAD EBOOK →Fundamentals of Statistical Signal Processing: Detection theory
Author: Steven M. Kay
Publisher: Pearson
Published: 1998
Total Pages: 584
ISBN-13:
DOWNLOAD EBOOK →V.2 Detection theory -- V.1 Estimation theory.
Fundamentals of Statistical Signal Processing: Estimation theory
Author: Steven M. Kay
Publisher:
Published: 2013
Total Pages: 0
ISBN-13: 9780134878409
DOWNLOAD EBOOK →"For those involved in the design and implementation of signal processing algorithms, this book strikes a balance between highly theoretical expositions and the more practical treatments, covering only those approaches necessary for obtaining an optimal estimator and analyzing its performance. Author Steven M. Kay discusses classical estimation followed by Bayesian estimation, and illustrates the theory with numerous pedagogical and real-world examples."--Cover, volume 1.
Statistical Signal Processing
Author: Louis L. Scharf
Publisher: Prentice Hall
Published: 1991
Total Pages: 552
ISBN-13:
DOWNLOAD EBOOK →This book embraces the many mathematical procedures that engineers and statisticians use to draw inference from imperfect or incomplete measurements. This book presents the fundamental ideas in statistical signal processing along four distinct lines: mathematical and statistical preliminaries; decision theory; estimation theory; and time series analysis.
Fundamentals Of Statistical Signal Processing Estimation Theory
Modern Spectral Estimation
An Introduction to Statistical Signal Processing
Author: Robert M. Gray
Publisher: Cambridge University Press
Published: 2004-12-02
Total Pages: 479
ISBN-13: 1139456288
DOWNLOAD EBOOK →This book describes the essential tools and techniques of statistical signal processing. At every stage theoretical ideas are linked to specific applications in communications and signal processing using a range of carefully chosen examples. The book begins with a development of basic probability, random objects, expectation, and second order moment theory followed by a wide variety of examples of the most popular random process models and their basic uses and properties. Specific applications to the analysis of random signals and systems for communicating, estimating, detecting, modulating, and other processing of signals are interspersed throughout the book. Hundreds of homework problems are included and the book is ideal for graduate students of electrical engineering and applied mathematics. It is also a useful reference for researchers in signal processing and communications.
An Introduction to Signal Detection and Estimation
Author: H. Vincent Poor
Publisher: Springer Science & Business Media
Published: 2013-06-29
Total Pages: 558
ISBN-13: 1475738633
DOWNLOAD EBOOK →The purpose of this book is to introduce the reader to the basic theory of signal detection and estimation. It is assumed that the reader has a working knowledge of applied probabil ity and random processes such as that taught in a typical first-semester graduate engineering course on these subjects. This material is covered, for example, in the book by Wong (1983) in this series. More advanced concepts in these areas are introduced where needed, primarily in Chapters VI and VII, where continuous-time problems are treated. This book is adapted from a one-semester, second-tier graduate course taught at the University of Illinois. However, this material can also be used for a shorter or first-tier course by restricting coverage to Chapters I through V, which for the most part can be read with a background of only the basics of applied probability, including random vectors and conditional expectations. Sufficient background for the latter option is given for exam pIe in the book by Thomas (1986), also in this series.
