Statistical Digital Signal Processing and Modeling

Monson H. Hayes

John Wiley & Sons, 1996

Table of Contents

Chapter 1 Introduction
Chapter 2 Background
2.1 Introduction
2.2 Discrete-Time Signal Processing
2.3 Linear Algebra
2.4 Summary
2.5 Problems
Chapter 3 Discrete-Time Random Processes
3.1 Introduction
3.2 Random Variables
3.3 Random Processes
3.4 Filtering Random Processes
3.5 Spectral Factorization
3.6 Special Types of Random Processes
3.7 Summary
3.8 Problems
Chapter 4 Signal Modeling
4.1 Introduction
4.2 The Least Squares (Direct) Method
4.3 The Pade Approximation
4.4 Prony's Method
4.5 Iterative Prefiltering*
4.6 Finite Data Records
4.7 Stochastic Models
4.8 Summary
4.9 Problems
Chapter 5 The Levinson Recursion
5.1 Introduction
5.2 The Levinson-Durbin Recursion
5.3 The Levinson Recursion
5.4 The Split Levinson Recursion*
5.5 Summary
5.6 Problems
Chapter 6 Lattice Filters
6.1 Introduction
6.2 The FIR Lattice Filter
6.3 Split Lattice Filter
6.4 IIR Lattice Filters
6.5 Lattice Methods for All-Pole Signal Modeling
6.6 Stochastic Modeling
6.7 Summary
6.8 Problems
Chapter 7 Optimum Filters
7.1 Introduction
7.2 The FIR Wiener Filter
7.3 The IIR Wiener Filter
7.4 Discrete Kalman Filter
7.5 Summary
7.6 Problems
Chapter 8 Spectrum Estimation
8.1 Introduction
8.2 Nonparametric Methods
8.3 Minimum Variance Spectrum Estimation
8.4 The Maximum Entropy Method
8.5 Parametric Methods
8.6 Frequency Estimation
8.7 Principal Components Frequency Estimation
8.8 Summary
8.9 Problems
Chapter 9 Adaptive Filtering
9.1 Introduction
9.2 FIR Adaptive Filters
9.3 Adaptive Recursive Filters
9.4 Recursive Least Squares
9.5 Summary
9.6 Problems
Appendix Using MATLAB Programs
A.1 Introduction
A.2 General Information
A.3 Random Processes
A.4 Signal Modeling
A.5 Levinson Recursion
A.6 Lattice Filters
A.7 Optimum Filters
A.8 Spectrum Estimation
A.9 Adaptive Filtering

Cover Page
Last updated: Jan. 10, 1996 by Prof. Monson H. Hayes
monty.hayes@ee.gatech.edu (Click to send a message)