Polyteknisk Boghandel

New Bayesian approach helps you solve tough problems in signal processing with ease

Preface
References. 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. Problems.
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. Problems.
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. Problems.
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. Problems.
5. Classical Bayesian State-Space Processors.
5.
1 Introduction
5.
2 Bayesian Approach to the State-Space.
5.
3 Linear Bayesian Processor (Linear Kalman Filter).
5.
4 Linearized Bayesian Processor (Linearized Kalman Filter).
5.
5 Extended Bayesian Processor (Extended Kalman Filter).
5.
6 Iterated-Extended Bayesian Processor (Iterated-Extended Kalman Filter).
5.
7 Practical Aspects of Classical Bayesian Processors.
5.
8 Case Study: RLC Circuit Problem.
5.
9 Summary. References. Problems.
6. Modern Bayesian State-Space Processors.
6.
1 Introduction
6.
2 Sigma-Point (Unscented) Transformations.
6.
3 Sigma-Point Bayesian Processor (Unscented Kalman Filter).
6.
4 Quadrature Bayesian Processors.
6.
5 Gaussian Sum (Mixture) Bayesian Processors.
6.
6 Case Study: 2D-Tracking Problem.
6.
7 Summary. References. Problems.
7. Particle-Based Bayesian State-Space Processors.
7.
1 Introduction
7.
2 Bayesian State-Space Particle Filters.
7.
3 Importance Proposal Distributions.
7.
4 Resampling.
7.
5 State-Space Particle Filtering Techniques.
7.
6 Practical Aspects of Particle Filter Design.
7.
7 Case Study: Population Growth Problem.
7.
8 Summary. References. Problems.
8. Joint Bayesian State/Parametric Processors.
8.
1 Introduction
8.
2 Bayesian Approach to Joint State/Parameter Estimation.
8.
3 Classical/Modern Joint Bayesian State/Parametric Processors.
8.
3.
1 Classical Joint Bayesian Processor.
8.
3.
2 Modern Joint Bayesian Processor.
8.
4 Particle-Based Joint Bayesian State/Parametric Processors.
8.
5 Case Study: Random Target Tracking using a Synthetic Aperture Towed Array.
8.
6 Summary. References. Problems.
9. Discrete Hidden Markov Model Bayesian Processors.
9.
1 Introduct