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Bayesian signal processing : classical, modern, and particle filtering methods /

Bayesian-based signal processing is expected to dominate the future of model-based signal processing for years to come. This book develops the 'Bayesian approach' to statistical signal processing for a variety of useful model sets with an emphasis on nonlinear/non-Gaussian problems, as wel...

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Detalles Bibliográficos
Clasificación:	Libro Electrónico
Autor principal:	Candy, James V. (Autor)
Formato:	Electrónico eBook
Idioma:	Inglés
Publicado:	Hoboken, New Jersey : John Wiley & Sons Inc., [2016]
Edición:	Second edition.
Colección:	Wiley series on adaptive and cognitive dynamic systems. Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control Ser. ; 54.
Temas:	Signal processing > Mathematics. Bayesian statistical decision theory. Traitement du signal > Mathématiques. Théorie de la décision bayésienne. TECHNOLOGY & ENGINEERING > Mechanical.
Acceso en línea:	Texto completo

Tabla de Contenidos:

Bayesian Signal Processing
Contents
Preface to Second Edition
References
Preface to First Edition
References
Acknowledgments
List of Abbreviations
1 Introduction
1.1 Introduction
1.2 Bayesian Signal Processing
1.3 Simulation-Based Approach to Bayesian Processing
1.3.1 Bayesian Particle Filter
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
2.3.1 Expectation-Maximization Approach to Maximum Likelihood
2.3.2 EM for Exponential Family of Distributions
2.4 Batch Minimum Variance Estimation
2.5 Sequential Bayesian Estimation
2.5.1 Joint Posterior Estimation
2.5.2 Filtering Posterior Estimation
2.5.3 Likelihood Estimation
2.6 Summary
References
3 Simulation-Based Bayesian Methods
3.1 Introduction
3.2 Probability Density Function Estimation
3.3 Sampling Theory
3.3.1 Uniform Sampling Method
3.3.2 Rejection Sampling Method
3.4 Monte Carlo Approach
3.4.1 Markov Chains
3.4.2 Metropolis-Hastings Sampling
3.4.3 Random Walk Metropolis-Hastings Sampling
3.4.4 Gibbs Sampling
3.4.5 Slice Sampling
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
4.4.1 Discrete Systems Theory
4.5 Gauss-Markov State-Space Models
4.5.1 Continuous-Time/Sampled-Data Gauss-Markov Models
4.5.2 Discrete-Time Gauss-Markov Models
4.6 Innovations Model
4.7 State-Space Model Structures
4.7.1 Time Series Models
4.7.2 State-Space and Time Series Equivalence Models.
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 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
6 Modern Bayesian State-Space Processors
6.1 Introduction
6.2 Sigma-Point (Unscented) Transformations
6.2.1 Statistical Linearization
6.2.2 Sigma-Point Approach
6.2.3 SPT for Gaussian Prior Distributions
6.3 Sigma-Point Bayesian Processor (Unscented Kalman Filter)
6.3.1 Extensions of the Sigma-Point Processor
6.4 Quadrature Bayesian Processors
6.5 Gaussian Sum (Mixture) Bayesian Processors
6.6 Case Study: 2D-Tracking Problem
6.7 Ensemble Bayesian Processors (Ensemble Kalman Filter)
6.8 Summary
References
7 Particle-Based Bayesian State-Space Processors
7.1 Introduction
7.2 Bayesian State-Space Particle Filters
7.3 Importance Proposal Distributions
7.3.1 Minimum Variance Importance Distribution
7.3.2 Transition Prior Importance Distribution
7.4 Resampling
7.4.1 Multinomial Resampling
7.4.2 Systematic Resampling
7.4.3 Residual Resampling
7.5 State-Space Particle Filtering Techniques
7.5.1 Bootstrap Particle Filter
7.5.2 Auxiliary Particle Filter
7.5.3 Regularized Particle Filter
7.5.4 MCMC Particle Filter
7.5.5 Linearized Particle Filter
7.6 Practical Aspects of Particle Filter Design
7.6.1 Sanity Testing
7.6.2 Ensemble Estimation
7.6.3 Posterior Probability Validation.
7.6.4 Model Validation Testing
7.7 Case Study: Population Growth Problem
7.8 Summary
References
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
8.4.1 Parametric Models
8.4.2 Joint Bayesian State/Parameter Estimation
8.5 Case Study: Random Target Tracking Using a Synthetic Aperture Towed Array
8.6 Summary
References
9 Discrete Hidden Markov Model Bayesian Processors
9.1 Introduction
9.2 Hidden Markov Models
9.2.1 Discrete-Time Markov Chains
9.2.2 Hidden Markov Chains
9.3 Properties of the Hidden Markov Model
9.4 HMM Observation Probability: Evaluation Problem
9.5 State Estimation in HMM: The Viterbi Technique
9.5.1 Individual Hidden State Estimation
9.5.2 Entire Hidden State Sequence Estimation
9.6 Parameter Estimation in HMM: The EM/Baum-Welch Technique
9.6.1 Parameter Estimation with State Sequence Known
9.6.2 Parameter Estimation with State Sequence Unknown
9.7 Case Study: Time-Reversal Decoding
9.8 Summary
References
10 Sequential Bayesian Detection
10.1 Introduction
10.2 Binary Detection Problem
10.2.1 Classical Detection
10.2.2 Bayesian Detection
10.2.3 Composite Binary Detection
10.3 Decision Criteria
10.3.1 Probability-of-Error Criterion
10.3.2 Bayes Risk Criterion
10.3.3 Neyman-Pearson Criterion
10.3.4 Multiple (Batch) Measurements
10.3.5 Multichannel Measurements
10.3.6 Multiple Hypotheses
10.4 Performance Metrics
10.4.1 Receiver Operating Characteristic (ROC) Curves
10.5 Sequential Detection
10.5.1 Sequential Decision Theory.
10.6 Model-Based Sequential Detection
10.6.1 Linear Gaussian Model-Based Processor
10.6.2 Nonlinear Gaussian Model-Based Processor
10.6.3 Non-Gaussian Model-Based Processor
10.7 Model-Based Change (Anomaly) Detection
10.7.1 Model-Based Detection
10.7.2 Optimal Innovations Detection
10.7.3 Practical Model-Based Change Detection
10.8 Case Study: Reentry Vehicle Change Detection
10.8.1 Simulation Results
10.9 Summary
References
11 Bayesian Processors for Physics-Based Applications
11.1 Optimal Position Estimation for the Automatic Alignment
11.1.1 Background
11.1.2 Stochastic Modeling of Position Measurements
11.1.3 Bayesian Position Estimation and Detection
11.1.4 Application: Beam Line Data
11.1.5 Results: Beam Line (KDP Deviation) Data
11.1.6 Results: Anomaly Detection
11.2 Sequential Detection of Broadband Ocean Acoustic Sources
11.2.1 Background
11.2.2 Broadband State-Space Ocean Acoustic Propagators
11.2.3 Discrete Normal-Mode State-Space Representation
11.2.4 Broadband Bayesian Processor
11.2.5 Broadband Particle Filters
11.2.6 Broadband Bootstrap Particle Filter
11.2.7 Bayesian Performance Metrics
11.2.8 Sequential Detection
11.2.9 Broadband BSP Design
11.2.10 Summary
11.3 Bayesian Processing for Biothreats
11.3.1 Background
11.3.2 Parameter Estimation
11.3.3 Bayesian Processor Design
11.3.4 Results
11.4 Bayesian Processing for the Detection of Radioactive Sources
11.4.1 Physics-Based Processing Model
11.4.2 Radionuclide Detection
11.4.3 Implementation
11.4.4 Detection
11.4.5 Data
11.4.6 Radionuclide Detection
11.4.7 Summary
11.5 Sequential Threat Detection: An X-ray Physics-Based Approach
11.5.1 Physics-Based Models
11.5.2 X-ray State-Space Simulation
11.5.3 Sequential Threat Detection
11.5.4 Summary.
11.6 Adaptive Processing for Shallow Ocean Applications
11.6.1 State-Space Propagator
11.6.2 Processors
11.6.3 Model-Based Ocean Acoustic Processing
11.6.4 Summary
References
Appendix: Probability and Statistics Overview
A.1 Probability Theory
A.2 Gaussian Random Vectors
A.3 Uncorrelated Transformation: Gaussian Random Vectors
References
Index
Wiley Series on Adaptive and Cognitive Dynamic Systems
EULA.

Bayesian signal processing : classical, modern, and particle filtering methods /

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