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Bayesian signal processing : classical, modern, and particle filtering methods / James V. Candy.

By: Candy, James V [author.].
Contributor(s): IEEE Xplore (Online Service) [distributor.] | Wiley [publisher.].
Material type: materialTypeLabelBookSeries: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control Ser: 54.Publisher: Hoboken, New Jersey : John Wiley & Sons Inc., [2016]Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2016]Edition: Second edition.Description: 1 PDF (640 pages).Content type: text Media type: electronic Carrier type: online resourceISBN: 9781119125471; 1119125456.Subject(s): Signal processing -- Mathematics | Bayesian statistical decision theoryGenre/Form: Electronic books.Additional physical formats: Print version:: Bayesian signal processing.DDC classification: 621.382/201519542 Online resources: Abstract with links to resource Also available in print.
Contents:
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.
Summary: 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 well as classical techniques.
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Includes index.

Includes bibliographical references at the end of each chapters and index.

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.

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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 well as classical techniques.

Also available in print.

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