Statistical & Adaptive Signal Processing / Edition 1

by Dimitris Manolakis, Stephen M. Kogon, Vinay K. Ingle

Overview

This authoritative volume on statistical and adaptive signal processing offers you a unified, comprehensive and practical treatment of spectral estimation, signal modeling, adaptive filtering, and array processing. Packed with over 3,000 equations and more than 300 illustrations, this unique resource provides you with balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional engineers and students alike.

Product Details

ISBN-13:	9781580536103
Publisher:	Artech House, Incorporated
Publication date:	04/30/2011
Series:	Artech House Signal Processing Library
Edition description:	New Edition
Pages:	816
Product dimensions:	1.69(w) x 8.50(h) x 11.00(d)

About the Author

Dimitris G. Manolakis is a member of the technical staff at M.I.T. Lincoln Laboratory. Previously, he was a principal member of the research staff at Riverside Research Institute. Dr. Manolakis has taught at the University of Athens, Northeastern University, Boston College, and Worchester Polytechnic Institute. He received his Ph.D. in electrical engineering from the University of Athens, Greece. Vinay K. Ingle is an associate professor of electrical and computer engineering at Northeastern University. He has broad research experience and has taught courses on signal and image processing, stochastic processes, and estimation theory. Dr. Ingle received his Ph.D. in electrical engineering from Rensselaer Polytechnic Institute. Stephen M. Kogon is a member of the technical staff at M.I.T. Lincoln Laboratory. Previously, he has been associated with Raytheon Co., Boston College, and Georgia Tech Research Institute. Dr. Kogon received his Ph.D. in electrical engineering from the Georgia Institute of Technology.

Table of Contents

Chapter 1: Introduction

Chapter 2: Fundamentals of Discrete-Time Signal Processing

Chapter 3: Random Variables, Vectors, and Sequences

Chapter 4: Linear Signal Models

Chapter 5: Nonparametric Power Spectrum Estimation

Chapter 6: Optimum Linear Filters

Chapter 7: Algorithms and Structure for Optimum Linear Filters

Chapter 8: Least-Squares Filtering and Prediction

Chapter 9: Signal Modeling and Parametric Spectral Estimation

Chapter 10: Adaptive Filters

Chapter 11: Array Processing

Chapter 12: Further Topics

Appendix A: Matrix Inversion Lemma

Appendix B: Gradients and Optimization in Complex Space

Appendix C: MATLAB Functions

Appendix D: Useful Results from Matrix Algebra

Appendix E: Minimum Phase Test for Polynomials