Procesamiento Estadístico de Señales Digitales – Monson Hayes – 1ra Edición

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