{"product_id":"bayesian-signal-processing-9781119125457","title":"Bayesian Signal Processing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003ePresents the Bayesian approach to statistical signal processing for a variety of useful model sets\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on Sequential Bayesian Detection, a new section on Ensemble Kalman Filters as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to fill-in-the gaps of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical sanity testing lead to ensemble techniques as a basic \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface to Second Edition xiii\u003c\/p\u003e \u003cp\u003eReferences xv\u003c\/p\u003e \u003cp\u003ePreface to First Edition xvii\u003c\/p\u003e \u003cp\u003eReferences xxiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xxvii\u003c\/p\u003e \u003cp\u003eList of Abbreviations xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Bayesian Signal Processing 1\u003c\/p\u003e \u003cp\u003e1.3 Simulation-Based Approach to Bayesian Processing 4\u003c\/p\u003e \u003cp\u003e1.3.1 Bayesian Particle Filter 8\u003c\/p\u003e \u003cp\u003e1.4 Bayesian Model-Based Signal Processing 9\u003c\/p\u003e \u003cp\u003e1.5 Notation and Terminology 13\u003c\/p\u003e \u003cp\u003eReferences 15\u003c\/p\u003e \u003cp\u003eProblems 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Bayesian Estimation 20\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 20\u003c\/p\u003e \u003cp\u003e2.2 Batch Bayesian Estimation 20\u003c\/p\u003e \u003cp\u003e2.3 Batch Maximum Likelihood Estimation 23\u003c\/p\u003e \u003cp\u003e2.3.1 Expectation–Maximization Approach to Maximum Likelihood 27\u003c\/p\u003e \u003cp\u003e2.3.2 EM for Exponential Family of Distributions 30\u003c\/p\u003e \u003cp\u003e2.4 Batch Minimum Variance Estimation 34\u003c\/p\u003e \u003cp\u003e2.5 Sequential Bayesian Estimation 37\u003c\/p\u003e \u003cp\u003e2.5.1 Joint Posterior Estimation 41\u003c\/p\u003e \u003cp\u003e2.5.2 Filtering Posterior Estimation 42\u003c\/p\u003e \u003cp\u003e2.5.3 Likelihood Estimation 45\u003c\/p\u003e \u003cp\u003e2.6 Summary 45\u003c\/p\u003e \u003cp\u003eReferences 46\u003c\/p\u003e \u003cp\u003eProblems 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Simulation-Based Bayesian Methods 52\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 52\u003c\/p\u003e \u003cp\u003e3.2 Probability Density Function Estimation 54\u003c\/p\u003e \u003cp\u003e3.3 Sampling Theory 58\u003c\/p\u003e \u003cp\u003e3.3.1 Uniform Sampling Method 60\u003c\/p\u003e \u003cp\u003e3.3.2 Rejection Sampling Method 64\u003c\/p\u003e \u003cp\u003e3.4 Monte Carlo Approach 66\u003c\/p\u003e \u003cp\u003e3.4.1 Markov Chains 71\u003c\/p\u003e \u003cp\u003e3.4.2 Metropolis–Hastings Sampling 74\u003c\/p\u003e \u003cp\u003e3.4.3 Random Walk Metropolis–Hastings Sampling 75\u003c\/p\u003e \u003cp\u003e3.4.4 Gibbs Sampling 79\u003c\/p\u003e \u003cp\u003e3.4.5 Slice Sampling 81\u003c\/p\u003e \u003cp\u003e3.5 Importance Sampling 83\u003c\/p\u003e \u003cp\u003e3.6 Sequential Importance Sampling 87\u003c\/p\u003e \u003cp\u003e3.7 Summary 90\u003c\/p\u003e \u003cp\u003eReferences 91\u003c\/p\u003e \u003cp\u003eProblems 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 State–Space Models for Bayesian Processing 98\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 98\u003c\/p\u003e \u003cp\u003e4.2 Continuous-Time State–Space Models 99\u003c\/p\u003e \u003cp\u003e4.3 Sampled-Data State–Space Models 103\u003c\/p\u003e \u003cp\u003e4.4 Discrete-Time State–Space Models 107\u003c\/p\u003e \u003cp\u003e4.4.1 Discrete Systems Theory 109\u003c\/p\u003e \u003cp\u003e4.5 Gauss–Markov State–Space Models 115\u003c\/p\u003e \u003cp\u003e4.5.1 Continuous-Time\/Sampled-Data Gauss–Markov Models 115\u003c\/p\u003e \u003cp\u003e4.5.2 Discrete-Time Gauss–Markov Models 117\u003c\/p\u003e \u003cp\u003e4.6 Innovations Model 123\u003c\/p\u003e \u003cp\u003e4.7 State–Space Model Structures 124\u003c\/p\u003e \u003cp\u003e4.7.1 Time Series Models 124\u003c\/p\u003e \u003cp\u003e4.7.2 State–Space and Time Series Equivalence Models 131\u003c\/p\u003e \u003cp\u003e4.8 Nonlinear (Approximate) Gauss–Markov State–Space Models 137\u003c\/p\u003e \u003cp\u003e4.9 Summary 142\u003c\/p\u003e \u003cp\u003eReferences 142\u003c\/p\u003e \u003cp\u003eProblems 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Classical Bayesian State–Space Processors 150\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 150\u003c\/p\u003e \u003cp\u003e5.2 Bayesian Approach to the State–Space 151\u003c\/p\u003e \u003cp\u003e5.3 Linear Bayesian Processor (Linear Kalman Filter) 153\u003c\/p\u003e \u003cp\u003e5.4 Linearized Bayesian Processor (Linearized Kalman Filter) 162\u003c\/p\u003e \u003cp\u003e5.5 Extended Bayesian Processor (Extended Kalman Filter) 170\u003c\/p\u003e \u003cp\u003e5.6 Iterated-Extended Bayesian Processor (Iterated-Extended Kalman Filter) 179\u003c\/p\u003e \u003cp\u003e5.7 Practical Aspects of Classical Bayesian Processors 185\u003c\/p\u003e \u003cp\u003e5.8 Case Study: \u003ci\u003eRLC \u003c\/i\u003eCircuit Problem 190\u003c\/p\u003e \u003cp\u003e5.9 Summary 194\u003c\/p\u003e \u003cp\u003eReferences 195\u003c\/p\u003e \u003cp\u003eProblems 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Modern Bayesian State–Space Processors 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 201\u003c\/p\u003e \u003cp\u003e6.2 Sigma-Point (Unscented) Transformations 202\u003c\/p\u003e \u003cp\u003e6.2.1 Statistical Linearization 202\u003c\/p\u003e \u003cp\u003e6.2.2 Sigma-Point Approach 205\u003c\/p\u003e \u003cp\u003e6.2.3 SPT for Gaussian Prior Distributions 210\u003c\/p\u003e \u003cp\u003e6.3 Sigma-Point Bayesian Processor (Unscented Kalman Filter) 213\u003c\/p\u003e \u003cp\u003e6.3.1 Extensions of the Sigma-Point Processor 222\u003c\/p\u003e \u003cp\u003e6.4 Quadrature Bayesian Processors 223\u003c\/p\u003e \u003cp\u003e6.5 Gaussian Sum (Mixture) Bayesian Processors 224\u003c\/p\u003e \u003cp\u003e6.6 Case Study: 2D-Tracking Problem 228\u003c\/p\u003e \u003cp\u003e6.7 Ensemble Bayesian Processors (Ensemble Kalman Filter) 234\u003c\/p\u003e \u003cp\u003e6.8 Summary 245\u003c\/p\u003e \u003cp\u003eReferences 247\u003c\/p\u003e \u003cp\u003eProblems 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Particle-Based Bayesian State–Space Processors 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 253\u003c\/p\u003e \u003cp\u003e7.2 Bayesian State–Space Particle Filters 253\u003c\/p\u003e \u003cp\u003e7.3 Importance Proposal Distributions 258\u003c\/p\u003e \u003cp\u003e7.3.1 Minimum Variance Importance Distribution 258\u003c\/p\u003e \u003cp\u003e7.3.2 Transition Prior Importance Distribution 261\u003c\/p\u003e \u003cp\u003e7.4 Resampling 262\u003c\/p\u003e \u003cp\u003e7.4.1 Multinomial Resampling 267\u003c\/p\u003e \u003cp\u003e7.4.2 Systematic Resampling 268\u003c\/p\u003e \u003cp\u003e7.4.3 Residual Resampling 269\u003c\/p\u003e \u003cp\u003e7.5 State–Space Particle Filtering Techniques 270\u003c\/p\u003e \u003cp\u003e7.5.1 Bootstrap Particle Filter 270\u003c\/p\u003e \u003cp\u003e7.5.2 Auxiliary Particle Filter 274\u003c\/p\u003e \u003cp\u003e7.5.3 Regularized Particle Filter 281\u003c\/p\u003e \u003cp\u003e7.5.4 MCMC Particle Filter 283\u003c\/p\u003e \u003cp\u003e7.5.5 Linearized Particle Filter 286\u003c\/p\u003e \u003cp\u003e7.6 Practical Aspects of Particle Filter Design 290\u003c\/p\u003e \u003cp\u003e7.6.1 Sanity Testing 290\u003c\/p\u003e \u003cp\u003e7.6.2 Ensemble Estimation 291\u003c\/p\u003e \u003cp\u003e7.6.3 Posterior Probability Validation 293\u003c\/p\u003e \u003cp\u003e7.6.4 Model Validation Testing 304\u003c\/p\u003e \u003cp\u003e7.7 Case Study: Population Growth Problem 311\u003c\/p\u003e \u003cp\u003e7.8 Summary 317\u003c\/p\u003e \u003cp\u003eReferences 318\u003c\/p\u003e \u003cp\u003eProblems 321\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Joint Bayesian State\/Parametric Processors 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 327\u003c\/p\u003e \u003cp\u003e8.2 Bayesian Approach to Joint State\/Parameter Estimation 328\u003c\/p\u003e \u003cp\u003e8.3 Classical\/Modern Joint Bayesian State\/Parametric Processors 330\u003c\/p\u003e \u003cp\u003e8.3.1 Classical Joint Bayesian Processor 331\u003c\/p\u003e \u003cp\u003e8.3.2 Modern Joint Bayesian Processor 338\u003c\/p\u003e \u003cp\u003e8.4 Particle-Based Joint Bayesian State\/Parametric Processors 341\u003c\/p\u003e \u003cp\u003e8.4.1 Parametric Models 342\u003c\/p\u003e \u003cp\u003e8.4.2 Joint Bayesian State\/Parameter Estimation 344\u003c\/p\u003e \u003cp\u003e8.5 Case Study: Random Target Tracking Using a Synthetic Aperture Towed Array 349\u003c\/p\u003e \u003cp\u003e8.6 Summary 359\u003c\/p\u003e \u003cp\u003eReferences 360\u003c\/p\u003e \u003cp\u003eProblems 362\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Discrete Hidden Markov Model Bayesian Processors 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 367\u003c\/p\u003e \u003cp\u003e9.2 Hidden Markov Models 367\u003c\/p\u003e \u003cp\u003e9.2.1 Discrete-Time Markov Chains 368\u003c\/p\u003e \u003cp\u003e9.2.2 Hidden Markov Chains 369\u003c\/p\u003e \u003cp\u003e9.3 Properties of the Hidden Markov Model 372\u003c\/p\u003e \u003cp\u003e9.4 HMM Observation Probability: Evaluation Problem 373\u003c\/p\u003e \u003cp\u003e9.5 State Estimation in HMM: The Viterbi Technique 376\u003c\/p\u003e \u003cp\u003e9.5.1 Individual Hidden State Estimation 377\u003c\/p\u003e \u003cp\u003e9.5.2 Entire Hidden State Sequence Estimation 380\u003c\/p\u003e \u003cp\u003e9.6 Parameter Estimation in HMM: The EM\/Baum–Welch Technique 384\u003c\/p\u003e \u003cp\u003e9.6.1 Parameter Estimation with State Sequence Known 385\u003c\/p\u003e \u003cp\u003e9.6.2 Parameter Estimation with State Sequence Unknown 387\u003c\/p\u003e \u003cp\u003e9.7 Case Study: Time-Reversal Decoding 390\u003c\/p\u003e \u003cp\u003e9.8 Summary 395\u003c\/p\u003e \u003cp\u003eReferences 396\u003c\/p\u003e \u003cp\u003eProblems 398\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Sequential Bayesian Detection 401\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 401\u003c\/p\u003e \u003cp\u003e10.2 Binary Detection Problem 402\u003c\/p\u003e \u003cp\u003e10.2.1 Classical Detection 403\u003c\/p\u003e \u003cp\u003e10.2.2 Bayesian Detection 407\u003c\/p\u003e \u003cp\u003e10.2.3 Composite Binary Detection 408\u003c\/p\u003e \u003cp\u003e10.3 Decision Criteria 411\u003c\/p\u003e \u003cp\u003e10.3.1 Probability-of-Error Criterion 411\u003c\/p\u003e \u003cp\u003e10.3.2 Bayes Risk Criterion 412\u003c\/p\u003e \u003cp\u003e10.3.3 Neyman–Pearson Criterion 414\u003c\/p\u003e \u003cp\u003e10.3.4 Multiple (Batch) Measurements 416\u003c\/p\u003e \u003cp\u003e10.3.5 Multichannel Measurements 418\u003c\/p\u003e \u003cp\u003e10.3.6 Multiple Hypotheses 420\u003c\/p\u003e \u003cp\u003e10.4 Performance Metrics 423\u003c\/p\u003e \u003cp\u003e10.4.1 Receiver Operating Characteristic (ROC) Curves 424\u003c\/p\u003e \u003cp\u003e10.5 Sequential Detection 440\u003c\/p\u003e \u003cp\u003e10.5.1 Sequential Decision Theory 442\u003c\/p\u003e \u003cp\u003e10.6 Model-Based Sequential Detection 447\u003c\/p\u003e \u003cp\u003e10.6.1 Linear Gaussian Model-Based Processor 447\u003c\/p\u003e \u003cp\u003e10.6.2 Nonlinear Gaussian Model-Based Processor 451\u003c\/p\u003e \u003cp\u003e10.6.3 Non-Gaussian Model-Based Processor 454\u003c\/p\u003e \u003cp\u003e10.7 Model-Based Change (Anomaly) Detection 459\u003c\/p\u003e \u003cp\u003e10.7.1 Model-Based Detection 460\u003c\/p\u003e \u003cp\u003e10.7.2 Optimal Innovations Detection 461\u003c\/p\u003e \u003cp\u003e10.7.3 Practical Model-Based Change Detection 463\u003c\/p\u003e \u003cp\u003e10.8 Case Study: Reentry Vehicle Change Detection 468\u003c\/p\u003e \u003cp\u003e10.8.1 Simulation Results 471\u003c\/p\u003e \u003cp\u003e10.9 Summary 472\u003c\/p\u003e \u003cp\u003eReferences 475\u003c\/p\u003e \u003cp\u003eProblems 477\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Bayesian Processors for Physics-Based Applications 484\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Optimal Position Estimation for the Automatic Alignment 484\u003c\/p\u003e \u003cp\u003e11.1.1 Background 485\u003c\/p\u003e \u003cp\u003e11.1.2 Stochastic Modeling of Position Measurements 487\u003c\/p\u003e \u003cp\u003e11.1.3 Bayesian Position Estimation and Detection 489\u003c\/p\u003e \u003cp\u003e11.1.4 Application: Beam Line Data 490\u003c\/p\u003e \u003cp\u003e11.1.5 Results: Beam Line (KDP Deviation) Data 492\u003c\/p\u003e \u003cp\u003e11.1.6 Results: Anomaly Detection 494\u003c\/p\u003e \u003cp\u003e11.2 Sequential Detection of Broadband Ocean Acoustic Sources 497\u003c\/p\u003e \u003cp\u003e11.2.1 Background 498\u003c\/p\u003e \u003cp\u003e11.2.2 Broadband State–Space Ocean Acoustic Propagators 500\u003c\/p\u003e \u003cp\u003e11.2.3 Discrete Normal-Mode State–Space Representation 504\u003c\/p\u003e \u003cp\u003e11.2.4 Broadband Bayesian Processor 504\u003c\/p\u003e \u003cp\u003e11.2.5 Broadband Particle Filters 505\u003c\/p\u003e \u003cp\u003e11.2.6 Broadband Bootstrap Particle Filter 507\u003c\/p\u003e \u003cp\u003e11.2.7 Bayesian Performance Metrics 509\u003c\/p\u003e \u003cp\u003e11.2.8 Sequential Detection 509\u003c\/p\u003e \u003cp\u003e11.2.9 Broadband BSP Design 512\u003c\/p\u003e \u003cp\u003e11.2.10 Summary 520\u003c\/p\u003e \u003cp\u003e11.3 Bayesian Processing for Biothreats 520\u003c\/p\u003e \u003cp\u003e11.3.1 Background 521\u003c\/p\u003e \u003cp\u003e11.3.2 Parameter Estimation 524\u003c\/p\u003e \u003cp\u003e11.3.3 Bayesian Processor Design 525\u003c\/p\u003e \u003cp\u003e11.3.4 Results 526\u003c\/p\u003e \u003cp\u003e11.4 Bayesian Processing for the Detection of Radioactive Sources 528\u003c\/p\u003e \u003cp\u003e11.4.1 Physics-Based Processing Model 528\u003c\/p\u003e \u003cp\u003e11.4.2 Radionuclide Detection 531\u003c\/p\u003e \u003cp\u003e11.4.3 Implementation 535\u003c\/p\u003e \u003cp\u003e11.4.4 Detection 539\u003c\/p\u003e \u003cp\u003e11.4.5 Data 540\u003c\/p\u003e \u003cp\u003e11.4.6 Radionuclide Detection 540\u003c\/p\u003e \u003cp\u003e11.4.7 Summary 541\u003c\/p\u003e \u003cp\u003e11.5 Sequential Threat Detection: An X-ray Physics-Based Approach 541\u003c\/p\u003e \u003cp\u003e11.5.1 Physics-Based Models 543\u003c\/p\u003e \u003cp\u003e11.5.2 X-ray State–Space Simulation 547\u003c\/p\u003e \u003cp\u003e11.5.3 Sequential Threat Detection 549\u003c\/p\u003e \u003cp\u003e11.5.4 Summary 554\u003c\/p\u003e \u003cp\u003e11.6 Adaptive Processing for Shallow Ocean Applications 554\u003c\/p\u003e \u003cp\u003e11.6.1 State–Space Propagator 555\u003c\/p\u003e \u003cp\u003e11.6.2 Processors 562\u003c\/p\u003e \u003cp\u003e11.6.3 Model-Based Ocean Acoustic Processing 565\u003c\/p\u003e \u003cp\u003e11.6.4 Summary 572\u003c\/p\u003e \u003cp\u003eReferences 572\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix: Probability and Statistics Overview 576\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Probability Theory 576\u003c\/p\u003e \u003cp\u003eA.2 Gaussian Random Vectors 582\u003c\/p\u003e \u003cp\u003eA.3 Uncorrelated Transformation: Gaussian Random Vectors 583\u003c\/p\u003e \u003cp\u003eReferences 584\u003c\/p\u003e \u003cp\u003eIndex 585\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406992548183,"sku":"9781119125457","price":106.16,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/bayesian-signal-processing-9781119125457","provider":"Book Curl","version":"1.0","type":"link"}