Bayesian signal processing: classical, modern, and particle filtering methods
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
Wiley
2009
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXIII, 445 S. graph. Darst. |
| ISBN: | 9780470180945 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804139250582552576 |
|---|---|
| adam_text | BAYESIAN SIGNAL PROCESSING CLASSICAL MODERN, AND PARTICLE FILTERING
METHODS JAMES V CANDY LAWRENCE LIVERMORE NATIONAL LABORATORY UNIVERSITY
OF CALIFORNIA SANTA BARBARA *IEEE WILEY AJOHNWILEY&SONS, INC.,
PUBLICATION CONTENTS PREFACE XIII REFERENCES TO THE PREFACE XIX
ACKNOWLEDGMENTS XXIII 1 INTRODUCTION 1 1.1 INTRODUCTION 1 1.2 BAYESIAN
SIGNAL PROCESSING 1 1.3 SIMULATION-BASED APPROACH TO BAYESIAN PROCESSING
4 1.4 BAYESIAN MODEL-BASED SIGNAL PROCESSING 8 1.5 NOTATION AND
TERMINOLOGY 12 REFERENCES 14 PROBLEMS 15 2 BAYESIAN ESTIMATION 19 2.1
INTRODUCTION 19 2.2 BATCH BAYESIAN ESTIMATION 19 2.3 BATCH MAXIMUM
LIKELIHOOD ESTIMATION 22 2.3.1 EXPECTATION-MAXIMIZATION APPROACH TO
MAXIMUM LIKELIHOOD 25 2.3.2 EM FOR EXPONENTIAL FAMILY OF DISTRIBUTIONS
30 2.4 BATCH MINIMUM VARIANCE ESTIMATION 33 2.5 SEQUENTIAL BAYESIAN
ESTIMATION 36 2.5.1 JOINT POSTERIOR ESTIMATION 39 2.5.2 FILTERING
POSTERIOR ESTIMATION 41 2.6 SUMMARY 43 REFERENCES 44 PROBLEMS 45 VII
VIII CONTENTS 3 SIMULATION-BASED BAYESIAN METHODS 51 3.1 INTRODUCTION
3.2 PROBABILITY DENSITY FUNCTION ESTIMATION 3.3 SAMPLING THEORY 3.3.1
UNIFORM S AMPLING 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 PROBLEMS 51 53 56 58 62 64 70 71 73 75
78 81 84 87 87 90 4 STATE-SPACE MODELS FOR BAYESIAN PROCESSING 95 4.1
INTRODUCTION 95 4.2 CONTINUOUS-TIME STATE-SPACE MODELS 96 4.3
SAMPLED-DATA STATE-SPACE MODELS 100 4.4 DISCRETE-TIME STATE-SPACE MODELS
104 4.4.1 DISCRETE SYSTEMS THEORY 107 4.5 GAUSS-MARKOV STATE-SPACE
MODELS 112 4.5.1 CONTINUOUS-TIME/SAMPLED-DATA GAUSS-MARKOV MODELS 112
4.5.2 DISCRETE-TIME GAUSS-MARKOV MODELS 114 4.6 INNOVATIONS MODEL 120
4.7 STATE-SPACE MODEL STRUCTURES 121 4.7.1 TIME SERIES MODELS 121 4.7.2
STATE-SPACE AND TIME SERIES EQUIVALENCE MODELS 129 4.8 NONLINEAR
(APPROXIMATE) GAUSS-MARKOV STATE-SPACE MODELS 135 4.9 SUMMARY 139
REFERENCES 140 PROBLEMS 141 5 CLASSICAL BAYESIAN STATE-SPACE PROCESSORS
147 5.1 INTRODUCTION 147 5.2 BAYESIAN APPROACH TO THE STATE-SPACE 147
5.3 LINEAR BAYESIAN PROCESSOR (LINEAR KAIMAN FILTER) 150 5.4 LINEARIZED
BAYESIAN PROCESSOR (LINEARIZED KAIMAN FILTER) 160 5.5 EXTENDED BAYESIAN
PROCESSOR (EXTENDED KAIMAN FILTER) 167 CONTENTS IX 5.6 ITERATED-EXTENDED
BAYESIAN PROCESSOR (ITERATED-EXTENDED KAIMAN FILTER) 174 5.7 PRACTICAL
ASPECTS OF CLASSICAL BAYESIAN PROCESSORS 182 5.8 CASE STUDY: RLC CIRCUIT
PROBLEM 186 5.9 SUMMARY 191 REFERENCES 191 PROBLEMS 193 6 MODERN
BAYESIAN STATE-SPACE PROCESSORS 197 6.1 INTRODUCTION 197 6.2 SIGMA-POINT
(UNSCENTED) TRANSFORMATIONS 198 6.2.1 STATISTICAL LINEARIZATION 198
6.2.2 SIGMA-POINT APPROACH 200 6.2.3 SPT FOR GAUSSIAN PRIOR
DISTRIBUTIONS 205 6.3 SIGMA-POINT BAYESIAN PROCESSOR (UNSCENTED KAIMAN
FILTER) 209 6.3.1 EXTENSIONS OF THE SIGMA-POINT PROCESSOR 218 6.4
QUADRATURE BAYESIAN PROCESSORS 218 6.5 GAUSSIAN SUM (MIXTURE) BAYESIAN
PROCESSORS 220 6.6 CASE STUDY: 2D-TRACKING PROBLEM 224 6.7 SUMMARY 230
REFERENCES 231 PROBLEMS 233 7 PARTICLE-BASED BAYESIAN STATE-SPACE
PROCESSORS 237 7.1 INTRODUCTION 237 7.2 BAYESIAN STATE-SPACE PARTICLE
FILTERS 237 7.3 IMPORTANCE PROPOSAL DISTRIBUTIONS 242 7.3.1 MINIMUM
VARIANCE IMPORTANCE DISTRIBUTION 242 7.3.2 TRANSITION PRIOR IMPORTANCE
DISTRIBUTION 245 7.4 RESAMPLING 246 7.4.1 MULTINOMIAL RESAMPLING 249
7.4.2 SYSTEMATIC RESAMPLING 251 7.4.3 RESIDUAL RESAMPLING 251 7.5
STATE-SPACE PARTICLE FILTERING TECHNIQUES 252 7.5.1 BOOTSTRAP PARTICLE
FILTER 253 7.5.2 AUXILIARY PARTICLE FILTER 261 7.5.3 REGULARIZED
PARTICLE FILTER 264 7.5.4 MCMC PARTICLE FILTER 266 7.5.5 LINEARIZED
PARTICLE FILTER 270 7.6 PRACTICAL ASPECTS OF PARTICLE FILTER DESIGN 272
7.6.1 POSTERIOR PROBABILITY VALIDATION 273 7.6.2 MODEL VALIDATION
TESTING 277 7.7 CASE STUDY: POPULATION GROWTH PROBLEM 285 7.8 SUMMARY
289 CONTENTS REFERENCES 290 PROBLEMS 293 JOINT BAYESIAN STATE/PARAMETRIC
PROCESSORS 299 8.1 INTRODUCTION 299 8.2 BAYESIAN APPROACH TO JOINT
STATE/PARAMETER ESTIMATION 300 8.3 CLASSICAL/MODERN JOINT BAYESIAN
STATE/PARAMETRIC PROCESSORS 302 8.3.1 CLASSICAL JOINT BAYESIAN PROCESSOR
303 8.3.2 MODERN JOINT BAYESIAN PROCESSOR 311 8.4 PARTICLE-BASED JOINT
BAYESIAN STATE/PARAMETRIC PROCESSORS 313 8.5 CASE STUDY: RANDOM TARGET
TRACKING USING A SYNTHETIC APERTURE TOWED ARRAY 318 8.6 SUMMARY 327
REFERENCES 328 PROBLEMS 330 DISCRETE HIDDEN MARKOV MODEL BAYESIAN
PROCESSORS 335 9.1 INTRODUCTION 335 9.2 HIDDEN MARKOV MODELS 335 9.2.1
DISCRETE-TIME MARKOV CHAINS 336 9.2.2 HIDDEN MARKOV CHAINS 337 9.3
PROPERTIES OF THE HIDDEN MARKOV MODEL 339 9.4 HMM OBSERVATION
PROBABILITY: EVALUATION PROBLEM 341 9.5 STATE ESTIMATION IN HMM: THE
VITERBI TECHNIQUE 345 9.5.1 INDIVIDUAL HIDDEN STATE ESTIMATION 345 9.5.2
ENTIRE HIDDEN STATE SEQUENCE ESTIMATION 347 9.6 PARAMETER ESTIMATION IN
HMM: THE EM/BAUM-WELCH TECHNIQUE 350 9.6.1 PARAMETER ESTIMATION WITH
STATE SEQUENCE KNOWN 352 9.6.2 PARAMETER ESTIMATION WITH STATE SEQUENCE
UNKNOWN 354 9.7 CASE STUDY: TIME-REVERSAL DECODING 357 9.8 SUMMARY 362
REFERENCES 363 PROBLEMS 365 BAYESIAN PROCESSORS FOR PHYSICS-BASED
APPLICATIONS 369 10.1 OPTIMAL POSITION ESTIMATION FOR THE AUTOMATIC
ALIGNMENT 369 10.1.1 BACKGROUND 369 10.1.2 STOCHASTIC MODELING OF
POSITION MEASUREMENTS 372 10.1.3 BAYESIAN POSITION ESTIMATION AND
DETECTION 374 10.1.4 APPLICATION: BEAM LINE DATA 375 10.1.5 RESULTS:
BEAM LINE (KDP DEVIATION) DATA 377 10.1.6 RESULTS: ANOMALY DETECTION 379
CONTENTS XI 10.2 BROADBAND OCEAN ACOUSTIC PROCESSING 382 10.2.1
BACKGROUND 382 10.2.2 BROADBAND STATE-SPACE OCEAN ACOUSTIC PROPAGATORS
384 10.2.3 BROADBAND BAYESIAN PROCESSING 389 10.2.4 BROADBAND BSP DESIG
N 393 10.2.5 RESULTS 395 10.3 BAYESIAN PROCESSING FOR BIOTHREATS 397
10.3.1 BACKGROUND 397 10.3.2 PARAMETER ESTIMATION 400 10.3.3 BAYESIAN
PROCESSOR DESIGN 401 10.3.4 RESULTS 403 10.4 BAYESIAN PROCESSING FOR THE
DETECTION OF RADIOACTIVE SOURCES 404 10.4.1 BACKGROUND 404 10.4.2
PHYSICS-BASED MODELS 404 10.4.3 GAMMA-RAY DETECTOR MEASUREMENTS 407
10.4.4 BAYESIAN PHYSICS-BASED PROCESSOR 410 10.4.5 PHYSICS-BASED
BAYESIAN DECONVOLUTION PROCESSOR 412 10.4.6 RESULTS 415 REFERENCES 417
APPENDIX A PROBABILITY & STATISTICS OVERVIEW A. 1 PROBABILITY THEORY A.2
GAUSSIAN RANDOM VECTORS A.3 UNCORRELATED TRANSFORMATION: GAUSSIAN RANDOM
VECTORS REFERENCES 423 423 429 430 430 INDEX 431
|
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| illustrated | Illustrated |
| indexdate | 2024-07-09T21:41:06Z |
| institution | BVB |
| isbn | 9780470180945 |
| language | English |
| lccn | 2008032184 |
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| physical | XXIII, 445 S. graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Wiley |
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| spelling | Candy, James V. 1944- Verfasser (DE-588)138283060 aut Bayesian signal processing classical, modern, and particle filtering methods James V. Candy New York Wiley 2009 XXIII, 445 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Mathematik Signal processing Mathematics Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Signalverarbeitung (DE-588)4054947-1 gnd rswk-swf Signalverarbeitung (DE-588)4054947-1 s Bayes-Verfahren (DE-588)4204326-8 s DE-604 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017644346&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Candy, James V. 1944- Bayesian signal processing classical, modern, and particle filtering methods Mathematik Signal processing Mathematics Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd Signalverarbeitung (DE-588)4054947-1 gnd |
| subject_GND | (DE-588)4204326-8 (DE-588)4054947-1 |
| title | Bayesian signal processing classical, modern, and particle filtering methods |
| title_auth | Bayesian signal processing classical, modern, and particle filtering methods |
| title_exact_search | Bayesian signal processing classical, modern, and particle filtering methods |
| title_full | Bayesian signal processing classical, modern, and particle filtering methods James V. Candy |
| title_fullStr | Bayesian signal processing classical, modern, and particle filtering methods James V. Candy |
| title_full_unstemmed | Bayesian signal processing classical, modern, and particle filtering methods James V. Candy |
| title_short | Bayesian signal processing |
| title_sort | bayesian signal processing classical modern and particle filtering methods |
| title_sub | classical, modern, and particle filtering methods |
| topic | Mathematik Signal processing Mathematics Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd Signalverarbeitung (DE-588)4054947-1 gnd |
| topic_facet | Mathematik Signal processing Mathematics Bayesian statistical decision theory Bayes-Verfahren Signalverarbeitung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017644346&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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