{"product_id":"bayesian-signal-processing-isbn-9781119125457","title":"Bayesian Signal Processing","description":"\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 requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation\/detection problems.\u003c\/p\u003e \u003cp\u003eThe second edition of \u003ci\u003eBayesian Signal Processing features\u003c\/i\u003e:\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003e“Classical” Kalman filtering for linear, linearized, and nonlinear systems; “modern” unscented and ensemble Kalman filters: and the “next-generation” Bayesian particle filters\u003c\/li\u003e \u003cli\u003eSequential Bayesian detection techniques incorporating model-based schemes for a variety of real-world problems\u003c\/li\u003e \u003cli\u003ePractical Bayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics\u003c\/li\u003e \u003cli\u003eNew case studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving\u003c\/li\u003e \u003cli\u003eMATLAB® notes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available\u003c\/li\u003e \u003cli\u003eProblem sets included to test readers’ knowledge and help them put their new skills into practice Bayesian \u003c\/li\u003e \u003c\/ul\u003e \u003ci\u003eSignal Processing, Second Edition\u003c\/i\u003e is written for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems. \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 \u003cp\u003e\u003cb\u003eJAMES V. CANDY, PhD, \u003c\/b\u003eis Chief Scientist for Engineering, a Distinguished Member of the Technical Staff, founder, and former director of the Center for Advanced Signal \u0026amp; Image Sciences at the Lawrence Livermore National Laboratory. He is also an Adjunct Full Professor at the University of California, Santa Barbara, a Fellow of the IEEE, and a Fellow of the Acoustical Society of America. Dr. Candy has published more than 225 journal articles, book chapters, and technical reports. He is also the author of \u003ci\u003eSignal Processing: Model-Based Approach\u003c\/i\u003e, \u003ci\u003eSignal Processing: A Modern Approach\u003c\/i\u003e, and \u003ci\u003eModel-Based Signal Processing \u003c\/i\u003e(Wiley 2006). Dr. Candy was awarded the IEEE Distinguished Technical Achievement Award for his development of model-based signal processing and the Acoustical Society of America Helmholtz-Rayleigh Interdisciplinary Silver Medal for his contributions to acoustical signal processing and underwater acoustics.\u003c\/p\u003e \u003cp\u003e \u003cb\u003ePresents the Bayesian approach to statistical signal processing for a variety of useful model sets\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book aims to give readers a unified Bayesian treatment starting from the basics (Bayes’ 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 \u003ci\u003eensemble techniques \u003c\/i\u003eas a basic requirement for performance analysis. The expansion of \u003ci\u003einformation theoretic metrics \u003c\/i\u003eand their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation\/detection problems.\u003c\/p\u003e \u003cp\u003eThe second edition of \u003ci\u003eBayesian Signal Processing \u003c\/i\u003efeatures:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e“Classical” Kalman filtering for linear, linearized, and nonlinear systems; “modern” unscented and \u003ci\u003eensemble \u003c\/i\u003eKalman filters; and the “next-generation” Bayesian particle filters\u003c\/li\u003e \u003cli\u003e\n\u003ci\u003eSequential Bayesian detection \u003c\/i\u003etechniques incorporating model-based schemes for a variety of real-world problems\u003c\/li\u003e \u003cli\u003e\n\u003ci\u003ePractical \u003c\/i\u003eBayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics\u003c\/li\u003e \u003cli\u003e\n\u003ci\u003eNew \u003c\/i\u003ecase studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving\u003c\/li\u003e \u003cli\u003e\n\u003ci\u003eMATLAB® \u003c\/i\u003enotes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available\u003c\/li\u003e \u003cli\u003e\n\u003ci\u003eProblem sets \u003c\/i\u003eto test readers’ knowledge and help them put their new skills into practice\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBayesian Signal Processing, Second Edition \u003c\/i\u003eis written for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47988794392805,"sku":"NP9781119125457","price":151.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119125457.jpg?v=1761781615","url":"https:\/\/k12savings.com\/products\/bayesian-signal-processing-isbn-9781119125457","provider":"K12savings","version":"1.0","type":"link"}