Introduction to random signals and applied Kalman filtering: with Matlab exercises
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2012
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| Ausgabe: | 4. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XII, 383 S. Illustrationen, Diagramme |
| ISBN: | 9780470609699 0470609699 |
Internformat
MARC
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| 100 | 1 | |a Brown, Robert Grover |d 1926- |e Verfasser |0 (DE-588)172925479 |4 aut | |
| 245 | 1 | 0 | |a Introduction to random signals and applied Kalman filtering |b with Matlab exercises |c Robert Grover Brown ; Patrick Y. C. Hwang |
| 250 | |a 4. ed. | ||
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2012 | |
| 300 | |a XII, 383 S. |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Stochastisches Signal |0 (DE-588)4140374-5 |2 gnd |9 rswk-swf |
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| 700 | 1 | |a Hwang, Patrick Y. C. |d ca. 20./21. Jh. |e Verfasser |0 (DE-588)1265615705 |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Passau - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024471946&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
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Datensatz im Suchindex
| _version_ | 1804148468436959232 |
|---|---|
| adam_text | Brief Contents v Preface PART 1 1 2 3 8 1 Probability and Random Variables: A Review 3 Mathematical Description of Random Signals 57 Linear Systems Response, State-Space Modeling, and Monte Carlo Simulation 105 PART 2 4 5 6 7 RANDOM SIGNALS BACKGROUND KALMAN FILTERING AND APPLICATIONS 139 141 Discrete Kalman Filter Basics Intermediate Topics on Kalman Filtering 173 Smoothing and Further Intermediate Topics 207 Linearization, Nonlinear Filtering, and Sampling Bayesian Filters 249 The “Go-Free” Concept, Complementary Filter, and Aided Inertial Examples 284 vii
Viii BRIEF CONTENTS 9 appendix appendix Kalman Filter Applications to the GPS and Other Navigation Systems A В Index 318 Laplace and Fourier Transforms 365 The Continuous Kalman Filter 371 379
Contents Preface v PART 1 1 RANDOM SIGNALS BACKGROUND 1 Probability and Random Variables: A Review 1.1 Random Signals 3 3 1.2 Intuitive Notion of Probability 1.3 Axiomatic Probability 1.4 Random Variables 1.5 Joint and Conditional Probability, Bayes Rule and Independence 9 1.6 Continuous Random Variables and Probability Density Function 13 4 5 8 1.7 Expectation, Averages, and Characteristic Function 1.8 Normal or Gaussian Random Variables 1.9 Impulsive Probability Density Functions 1.10 Joint Continuous Random Variables 15 18 22 23 1.11 Correlation, Covariance, and Orthogonality 1.12 Sum of Independent Random Variables and Tendency Toward Normal Distribution 28 1.13 Transformation of Random Variables 32 1.14 Multivariate Normal Density Function 37 1.15 Linear Transformation and General Properties of Normal Random Variables 40 1.16 Limits, Convergence, and Unbiased Estimators 1.17 A Note on Statistical Estimators 26 43 46 ix
X CONTENTS 2 3 Mathematical Description of Random Signals Concept of a Random Process 2.2 Probabilistic Description of a Random Process 2.3 Gaussian Random Process 2.4 Stationarity, Ergodicity, and Classification of Processes 2.5 Autocorrelation Function Crosscorrelation Function 60 62 63 65 68 2.7 Power Spectral Density Function 2.8 White Noise 2.9 Gauss-Markov Processes 2.10 Narrowband Gaussian Process 70 75 77 81 83 2.11 Wiener or Brownian-Motion Process 2.12 Pseudorandom Signals 2.13 Determination of Autocorrelation and Spectral Density Functions from Experimental Data 90 2.14 Sampling Theorem 86 95 Linear Systems Response, State-Space Modeling, and Monte Carlo Simulation 3.1 Introduction: The Analysis Problem 105 3.2 Stationary (Steady-State) Analysis 106 3.3 Integral Tables for Computing Mean-Square Value 3.4 Pure White Noise and Bandlimited Systems 3.5 Noise Equivalent Bandwidth 3.6 Shaping Filter 105 109 110 111 113 3.7 Nonstationary (Transient) Analysis 3.8 Note on Units and Unity White Noise 3.9 Vector Description of Random Processes 3.10 Monte Carlo Simulation of Discrete-Time Processes 3.11 Summary PART 2 4 57 2.1 2.6 57 114 118 121 128 130 KALMAN FILTERING AND APPLICATIONS Discrete Kalman Filter Basics 4.1 A Simple Recursive Example 4.2 The Discrete Kalman Filter 141 143 139 141
CONTENTS 5 6 7 4.3 Simple Kalman Filter Examples and Augmenting the State Vector 4.4 Marine Navigation Application with Multiple-Inputs/Multiple-Outputs 4.5 Gaussian Monte Carlo Examples 4.6 Prediction 4.7 The Conditional Density Viewpoint 4.8 Re-cap and Special Note On Updating the Error Covariance Matrix XÌ 148 151 154 159 162 Intermediate Topics on Kalman Filtering 165 173 5.1 Alternative Form of the Discrete Kalman Filter - the Information Filter 5.2 Processing the Measurements One at a Time 5.3 Orthogonality Principle 5.4 Divergence Problems 5.5 Suboptimal Error Analysis 5.6 Reduced-Order Suboptimality 5.7 Square-Root Filtering and U-D Factorization 5.8 Kalman Filter Stability 5.9 Relationship to Deterministic Least Squares Estimation 5.10 Deterministic Inputs 173 176 178 181 184 188 193 197 198 201 207 Smoothing and Further Intermediate Topics 6.1 Classification of smoothing Problems 6.2 Discrete Fixed-Interval Smoothing 6.3 Discrete Fixed-Point Smoothing 207 208 212 213 6.4 Discrete Fixed-Lag Smoothing 6.5 Adaptive Kalman Filter (Multiple Model Adaptive Estimator) 6.6 Correlated Process and Measurement Noise for the Discrete Filter—DelayedState Filter Algorithm 226 6.7 Decentralized Kalman Filtering 6.8 Difficulty with Hard-Bandlimited Processes 6.9 The Recursive Bayesian Filter 231 234 237 Linearization, Nonlinear Filtering, and Sampling Bayesian Filters 249 7.1 Linearization 7.2 The Extended Kalman Filter 7.3 “Beyond the Kalman Filter” 216 257 260 249
Xii CONTENTS 8 9 7.4 Тһө Ensemble Kalman Filter 262 7.5 The Unscented Kalman Filter 265 7.6 The Partide Filter 269 The “Go-Free” Concept, Complementary Filter, and Aided Inertial Examples 8.1 Introduction. Why Go Free of Anything? 8.2 Simple GPS Clock Bias Model 8.3 Euler/Goad Experiment 8.4 Reprise: GPS Clock-Bias Model Revisited 8.5 The Complementary Filter 8.6 Simple Complementary Filter: Intuitive Method 8.7 Kalman Filter Approach—Error Model 294 8.8 Kalman Filter Approach—Total Model 296 8.9 Go-Free Monte Carlo Simulation 284 284 285 287 289 290 292 298 8.10 INS Error Models 8.11 Aiding with Positioning Measurements—INS/DME Measurement Model 8.12 Other Integration Considerations and Concluding Remarks 303 309 Kalman Filter Applications to the GPS and Other Navigation Systems 9.1 Position Determination with GPS 9.2 The Observables 307 318 318 321 9.3 Basic Position and Time Process Models 9.4 Modeling of Different Carrier Phase Measurements and Ranging Errors 9.5 GPS-Aided Inertial Error Models 9.6 Communication Link Ranging and Timing 9.7 Simultaneous Localization and Mapping (SLAM) 9.8 Closing Remarks 324 330 339 345 348 352 appendix A Laplace and Fourier Transforms 365 appendix В The Continuous Kalman Filter 371 Index 379
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| author | Brown, Robert Grover 1926- Hwang, Patrick Y. C. ca. 20./21. Jh |
| author_GND | (DE-588)172925479 (DE-588)1265615705 |
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| dewey-full | 621.382/2 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.382/2 |
| dewey-search | 621.382/2 |
| dewey-sort | 3621.382 12 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Energietechnik, Energiewirtschaft Elektrotechnik Elektrotechnik / Elektronik / Nachrichtentechnik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik |
| edition | 4. ed. |
| format | Book |
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| id | DE-604.BV039621553 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:07:37Z |
| institution | BVB |
| isbn | 9780470609699 0470609699 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024471946 |
| oclc_num | 711044717 |
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| physical | XII, 383 S. Illustrationen, Diagramme |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Wiley |
| record_format | marc |
| spelling | Brown, Robert Grover 1926- Verfasser (DE-588)172925479 aut Introduction to random signals and applied Kalman filtering with Matlab exercises Robert Grover Brown ; Patrick Y. C. Hwang 4. ed. Hoboken, NJ Wiley 2012 XII, 383 S. Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Stochastisches Signal (DE-588)4140374-5 gnd rswk-swf Kalman-Filter (DE-588)4130759-8 gnd rswk-swf Kalman-Filter (DE-588)4130759-8 s Stochastisches Signal (DE-588)4140374-5 s DE-604 Hwang, Patrick Y. C. ca. 20./21. Jh. Verfasser (DE-588)1265615705 aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024471946&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Brown, Robert Grover 1926- Hwang, Patrick Y. C. ca. 20./21. Jh Introduction to random signals and applied Kalman filtering with Matlab exercises Stochastisches Signal (DE-588)4140374-5 gnd Kalman-Filter (DE-588)4130759-8 gnd |
| subject_GND | (DE-588)4140374-5 (DE-588)4130759-8 |
| title | Introduction to random signals and applied Kalman filtering with Matlab exercises |
| title_auth | Introduction to random signals and applied Kalman filtering with Matlab exercises |
| title_exact_search | Introduction to random signals and applied Kalman filtering with Matlab exercises |
| title_full | Introduction to random signals and applied Kalman filtering with Matlab exercises Robert Grover Brown ; Patrick Y. C. Hwang |
| title_fullStr | Introduction to random signals and applied Kalman filtering with Matlab exercises Robert Grover Brown ; Patrick Y. C. Hwang |
| title_full_unstemmed | Introduction to random signals and applied Kalman filtering with Matlab exercises Robert Grover Brown ; Patrick Y. C. Hwang |
| title_short | Introduction to random signals and applied Kalman filtering |
| title_sort | introduction to random signals and applied kalman filtering with matlab exercises |
| title_sub | with Matlab exercises |
| topic | Stochastisches Signal (DE-588)4140374-5 gnd Kalman-Filter (DE-588)4130759-8 gnd |
| topic_facet | Stochastisches Signal Kalman-Filter |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024471946&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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