Book description
A problemsolving approach to statistical signal processing for practicing engineers, technicians, and graduate students
This book takes a pragmatic approach in solving a set of common problems engineers and technicians encounter when processing signals. In writing it, the author drew on his vast theoretical and practical experience in the field to provide a quicksolution manual for technicians and engineers, offering fieldtested solutions to most problems engineers can encounter. At the same time, the book delineates the basic concepts and applied mathematics underlying each solution so that readers can go deeper into the theory to gain a better idea of the solution’s limitations and potential pitfalls, and thus tailor the best solution for the specific engineering application.
Uniquely, Statistical Signal Processing in Engineering can also function as a textbook for engineering graduates and postgraduates. Dr. Spagnolini, who has had a quarter of a century of experience teaching graduatelevel courses in digital and statistical signal processing methods, provides a detailed axiomatic presentation of the conceptual and mathematical foundations of statistical signal processing that will challenge students’ analytical skills and motivate them to develop new applications on their own, or better understand the motivation underlining the existing solutions.
Throughout the book, some realworld examples demonstrate how powerful a tool statistical signal processing is in practice across a wide range of applications.
 Takes an interdisciplinary approach, integrating basic concepts and tools for statistical signal processing
 Informed by its author’s vast experience as both a practitioner and teacher
 Offers a handson approach to solving problems in statistical signal processing
 Covers a broad range of applications, including communication systems, machine learning, wavefield and array processing, remote sensing, image filtering and distributed computations
 Features numerous realworld examples from a wide range of applications showing the mathematical concepts involved in practice
 Includes MATLAB code of many of the experiments in the book
Statistical Signal Processing in Engineering is an indispensable working resource for electrical engineers, especially those working in the information and communication technology (ICT) industry. It is also an ideal text for engineering students at large, applied mathematics postgraduates and advanced undergraduates in electrical engineering, applied statistics, and pure mathematics, studying statistical signal processing.
Table of contents
 Cover
 Title Page
 List of Figures
 List of Tables
 Preface
 List of Abbreviations
 How to Use the Book
 About the Companion Website
 Prerequisites
 Why are there so many matrixes in this book?
 1 Manipulations on Matrixes

2 Linear Algebraic Systems
 2.1 Problem Definition and Vector Spaces
 2.2 Rotations
 2.3 Projection Matrixes and Data‐Filtering
 2.4 Singular Value Decomposition (SVD) and Subspaces
 2.5 QR and Cholesky Factorization
 2.6 Power Method for Leading Eigenvectors
 2.7 Least Squares Solution of Overdetermined Linear Equations
 2.8 Efficient Implementation of the LS Solution
 2.9 Iterative Methods

3 Random Variables in Brief
 3.1 Probability Density Function (pdf), Moments, and Other Useful Properties
 3.2 Convexity and Jensen Inequality
 3.3 Uncorrelatedness and Statistical Independence
 3.4 Real‐Valued Gaussian Random Variables
 3.5 Conditional pdf for Real‐Valued Gaussian Random Variables
 3.6 Conditional pdf in Additive Noise Model
 3.7 Complex Gaussian Random Variables
 3.8 Sum of Square of Gaussians: Chi‐Square
 3.9 Order Statistics for N rvs

4 Random Processes and Linear Systems
 4.1 Moment Characterizations and Stationarity
 4.2 Random Processes and Linear Systems
 4.3 Complex‐Valued Random Processes
 4.4 Pole‐Zero and Rational Spectra (Discrete‐Time)
 4.5 Gaussian Random Process (Discrete‐Time)
 4.6 Measuring Moments in Stochastic Processes
 Appendix A: Transforms for Continuous‐Time Signals
 Appendix B: Transforms for Discrete‐Time Signals
 5 Models and Applications
 6 Estimation Theory
 7 Parameter Estimation
 8 Cramér–Rao Bound
 9 MLE and CRB for Some Selected Cases
 10 Numerical Analysis and Montecarlo Simulations
 11 Bayesian Estimation
 12 Optimal Filtering
 13 Bayesian Tracking and Kalman Filter

14 Spectral Analysis
 14.1 Periodogram
 14.2 Parametric Spectral Analysis
 14.3 AR Spectral Analysis
 14.4 MA Spectral Analysis
 14.5 ARMA Spectral Analysis
 Appendix A: Which Sample Estimate of the Autocorrelation to Use?
 Appendix B: Eigenvectors and Eigenvalues of Correlation Matrix
 Appendix C: Property of Monic Polynomial
 Appendix D: Variance of Pole in AR(1)

15 Adaptive Filtering
 15.1 Adaptive Interference Cancellation
 15.2 Adaptive Equalization in Communication Systems
 15.3 Steepest Descent MSE Minimization
 15.4 From Iterative to Adaptive Filters
 15.5 LMS Algorithm and Stochastic Gradient
 15.6 Convergence Analysis of LMS Algorithm
 15.7 Learning Curve of LMS
 15.8 NLMS Updating and Non‐Stationarity
 15.9 Numerical Example: Adaptive Identification
 15.10 RLS Algorithm
 15.11 Exponentially‐Weighted RLS
 15.12 LMS vs. RLS
 Appendix A: Convergence in Mean Square
 16 Line Spectrum Analysis
 17 Equalization in Communication Engineering
 18 2D Signals and Physical Filters
 19 Array Processing

20 Multichannel Time of Delay Estimation
 20.1 Model Definition for ToD
 20.2 High Resolution Method for ToD (L = 1)
 20.3 Difference of ToD (DToD) Estimation
 20.4 Numerical Performance Analysis of DToD
 20.5 Wavefront Estimation: Non‐Parametric Method (L = 1)
 20.6 Parametric ToD Estimation and Wideband Beamforming
 Appendix A: Properties of the Sample Correlations
 Appendix B: How to Delay a Discrete‐Time Signal?
 Appendix C: Wavefront Estimation for 2D Arrays
 21 Tomography
 22 Cooperative Estimation
 23 Classification and Clustering
 References
 Index
 End User License Agreement
Product information
 Title: Statistical Signal Processing in Engineering
 Author(s):
 Release date: February 2018
 Publisher(s): Wiley
 ISBN: 9781119293972
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