Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods

Contents

Preface

References to the Preface

Acknowledgments

1 Introduction

1.1 Introduction

1.2 Bayesian Signal Processing

1.3 Simulation-Based Approach to Bayesian Processing

1.4 Bayesian Model-Based Signal Processing

1.5 Notation and Terminology

References

2 Bayesian Estimation

2.1 Introduction

2.2 Batch Bayesian Estimation

2.3 Batch Maximum Likelihood Estimation

2.4 Batch Minimum Variance Estimation

2.5 Sequential Bayesian Estimation

2.6 Summary

References

3 Simulation-Based Bayesian Methods

3.1 Introduction

3.2 Probability Density Function Estimation

3.3 Sampling Theory

3.4 Monte Carlo Approach

3.5 Importance Sampling

3.6 Sequential Importance Sampling

3.7 Summary

References

4 State-Space Models for Bayesian Processing

4.1 Introduction

4.2 Continuous-Time State-Space Models

4.3 Sampled-Data State-Space Models

4.4 Discrete-Time State-Space Models

4.5 Gauss-Markov State-Space Models

4.6 Innovations Model

4.7 State-Space Model Structures

4.8 Nonlinear (Approximate) Gauss-Markov State-Space Models

4.9 Summary

References

5 Classical Bayesian State-Space Processors

5.1 Introduction

5.2 Bayesian Approach to the State-Space

5.3 Linear Bayesian Processor (Linear Kaiman Filter)

5.4 Linearized Bayesian Processor (Linearized Kaiman Filter)

5.5 Extended Bayesian Processor (Extended Kaiman Filter)

5.6 Iterated-Extended Bayesian Processor (Iterated-Extended Kaiman Filter)

5.7 Practical Aspects of Classical Bayesian Processors

5.8 Case Study: RLC Circuit Problem

5.9 Summary

References

6 Modern ...