Particle filters for random set models:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York ; Heidelberg ; Dordrecht ; London
Springer
[2013]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIV, 174 Seiten Diagramme |
| ISBN: | 9781461463153 |
Internformat
MARC
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| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Computer science | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Mathematics | |
| 650 | 4 | |a Engineering | |
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Datensatz im Suchindex
| _version_ | 1804176236704956416 |
|---|---|
| adam_text | Contents
1 Introduction........................................................... 1
References............................................................ 3
2 Background............................................................. 5
2.1 A Brief Review of Particle Filters............................... 5
2.2 Online Sensor Control............................................ 6
2.3 Non-standard Measurements....................................... 10
2.3.1 Imprecise Measurements................................... 10
2.3.2 Imprecise Measurement Function........................... 11
2.3.3 Uncertain Implication Rules.............................. 12
2.3.4 Particle Filter Implementation........................... 13
2.3.5 Applications............................................. 14
2.4 Multiple Objects and Imperfect Detection........................ 15
2.4.1 Random Finite Sets....................................... 15
2.4.2 Multi-Object Stochastic Filtering........................ 19
2.4.3 OSPA Metric.............................................. 20
2.5 Specialized Multi-Object Filters................................ 22
2.5.1 Bernoulli Filter......................................... 22
2.5.2 PHD and CPHD Filter...................................... 24
References........................................................... 26
3 Applications Involving Non-standard Measurements...................... 29
3.1 Estimation Using Imprecise Measurement Models................... 29
3.1.1 Localization Using the Received Signal Strength.......... 29
3.1.2 Prediction of an Epidemic Using Syndromic Data........... 34
3.1.3 Summary.................................................. 39
3.2 Fusion of Spatially Referring Natural Language Statements .... 39
3.2.1 Language, Space, and Modeling............................ 40
3.2.2 An Illustrative Example.................................. 42
3.3 Classification Using Imprecise Likelihoods...................... 44
3.3.1 Modeling................................................. 45
3.3.2 Classification Results................................... 47
References........................................................... 50
vii
viii Contents
4 Multi-Object Particle Filters........................................ 53
4.1 Bernoulli Particle Filters..................................... 53
4.1.1 Standard Bernoulli Particle Filters...................... 53
4.1.2 Bernoulli Box-Particle Filter............................ 56
4.2 PHD/CPDH Particle Filters with Adaptive Birth Intensity.... 62
4.2.1 Extension of the PHD Filter.............................. 63
4.2.2 Extension of the CPHD Filter............................. 66
4.2.3 Implementation........................................... 67
4.2.4 A Numerical Study........................................ 72
4.2.5 State Estimation from PHD/CPHD Particle Filters...... 76
4.3 Particle Filter Approximation of the Exact
Multi-Object Filter............................................. 79
References........................................................... 83
5 Sensor Control for Random Set Based Particle Filters................. 85
5.1 Bernoulli Particle Filter with Sensor Control.................. 85
5.1.1 The Reward Function...................................... 85
5.1.2 Bearings Only Tracking in Clutter
with Observer Control................................ 87
5.1.3 Target Tracking via Multi-Static Doppler Shifts.......... 96
5.2 Sensor Control for PHD/CPHD Particle Filters.................. 104
5.2.1 The Reward Function..................................... 105
5.2.2 A Numerical Study....................................... 108
5.3 Sensor Control for the Multi-Target State Particle Filter..... 112
5.3.1 Particle Approximation of the Reward Function........... 112
5.3.2 A Numerical Study....................................... 113
References.......................................................... 118
6 Multi-Target Tracking............................................... 121
6.1 OSPA-T: A Performance Metric for Multi-Target Tracking .... 122
6.1.1 The Problem and Its Conceptual Solution................. 123
6.1.2 The Base Distance and Labeling of Estimated Tracks . . . 125
6.1.3 Numerical Examples...................................... 128
6.2 Trackers Based on Random Set Filters.......................... 132
6.2.1 Multi-Target Trackers Based on the Bernoulli PF......... 132
6.2.2 Multi-Target Trackers Based on the PHD
Particle Filter......................................... 135
6.2.3 Error Performance Comparison Using
the OSPA-T Error........................................ 136
6.3 Application: Pedestrian Tracking.............................. 137
6.3.1 Video Dataset and Detections............................ 138
6.3.2 Description of Algorithms............................... 139
6.3.3 Numerical Results....................................... 141
References.......................................................... 142
Contents
IX
7 Advanced Topics...................................................... 145
7.1 Bernoulli Filter for Extended Target Tracking.................. 145
7.1.1 Mathematical Models..................................... 146
7.1.2 Equations of the Bernoulli Filter
for an Extended Target.................................. 148
7.1.3 Numerical Implementation................................ 150
7.1.4 Simulation Results...................................... 152
7.1.5 Application to a Surveillance Video..................... 155
7.2 Calibration of Tracking Systems................................ 157
7.2.1 Background and Problem Formulation...................... 160
7.2.2 The Proposed Calibration Algorithm...................... 161
7.2.3 Importance Sampling with Progressive Correction....... 163
7.2.4 Application to Sensor Bias Estimation................... 165
References.......................................................... 171
Index................................................................... 173
|
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| id | DE-604.BV043564658 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:28:59Z |
| institution | BVB |
| isbn | 9781461463153 |
| language | English |
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| physical | XIV, 174 Seiten Diagramme |
| publishDate | 2013 |
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| publishDateSort | 2013 |
| publisher | Springer |
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| spelling | Ristic, Branko Verfasser (DE-588)1029448620 aut Particle filters for random set models Branko Ristic New York ; Heidelberg ; Dordrecht ; London Springer [2013] 2013 XIV, 174 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Informatik Ingenieurwissenschaften Künstliche Intelligenz Mathematik Computer science Artificial intelligence Mathematics Engineering BAYES (DE-588)4245375-6 gnd rswk-swf Wahrscheinlichkeit (DE-588)4137007-7 gnd rswk-swf BAYES (DE-588)4245375-6 s Wahrscheinlichkeit (DE-588)4137007-7 s DE-604 Erscheint auch als Online-Ausgabe 978-1-4614-6316-0 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028979672&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ristic, Branko Particle filters for random set models Informatik Ingenieurwissenschaften Künstliche Intelligenz Mathematik Computer science Artificial intelligence Mathematics Engineering BAYES (DE-588)4245375-6 gnd Wahrscheinlichkeit (DE-588)4137007-7 gnd |
| subject_GND | (DE-588)4245375-6 (DE-588)4137007-7 |
| title | Particle filters for random set models |
| title_auth | Particle filters for random set models |
| title_exact_search | Particle filters for random set models |
| title_full | Particle filters for random set models Branko Ristic |
| title_fullStr | Particle filters for random set models Branko Ristic |
| title_full_unstemmed | Particle filters for random set models Branko Ristic |
| title_short | Particle filters for random set models |
| title_sort | particle filters for random set models |
| topic | Informatik Ingenieurwissenschaften Künstliche Intelligenz Mathematik Computer science Artificial intelligence Mathematics Engineering BAYES (DE-588)4245375-6 gnd Wahrscheinlichkeit (DE-588)4137007-7 gnd |
| topic_facet | Informatik Ingenieurwissenschaften Künstliche Intelligenz Mathematik Computer science Artificial intelligence Mathematics Engineering BAYES Wahrscheinlichkeit |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028979672&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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