Tracking and sensor data fusion: methodological framework and selected applications
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg [u.a.]
Springer
2014
|
| Series: | Mathematical engineering
|
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis Klappentext |
| Physical Description: | XVII, 253 S. Ill., graph. Darst. 235 mm x 155 mm |
| ISBN: | 9783642392719 9783642392702 |
Staff View
MARC
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CONTENTS
1 NOTION AND STRUCTURE OF SENSOR DATA FUSION 1
1.1 SUBJECT MATTER 1
1.1.1 ORIGINS OF MODERN DEVELOPMENT 2
1.1.2 GENERAL TECHNOLOGICAL PREREQUISITES 3
1.1.3 RELATION TO INFORMATION SYSTEMS 4
1.2 CHARACTERIZATION AS A BRANCH OF APPLIED SCIENCE 7
1.2.1 PIONEERS OF SENSOR DATA FUSION 7
1.2.2 ORGANIZATION OF THE RESEARCH COMMUNITY 11
1.2.3 IMPORTANT PUBLICATION PLATFORMS 11
1.3 FROM IMPERFECT DATA TO SITUATION PICTURES 11
1.3.1 DISCUSSION OF CHARACTERISTIC ASPECTS 13
1.3.2 REMARKS ON THE METHODS USED 13
1.3.3 A GENERIC SENSOR DATA FUSION SYSTEM 14
1.3.4 ON MEASURING FUSION PERFORMANCE 16
1.3.5 TRACKING-DERIVED SITUATION ELEMENTS 17
1.3.6 SELECTED ISSUES IN ANOMALY DETECTION 19
1.4 FUTURE PERSPECTIVES OF SENSOR DATA FUSION 22
1.4.1 NEW EVERYDAY LIFE APPLICATIONS 22
1.4.2 DISCUSSION OF LARGE-SCALE TRENDS 23
REFERENCES 25
PART I SENSOR DATA FUSION: METHODOLOGICAL FRAMEWORK
2 CHARACTERIZING OBJECTS AND SENSORS 31
2.1 EXAMPLES OF STATE QUANTITIES 31
2.2 OBJECT EVOLUTION MODELS 33
2.2.1 VAN-KEUK'S EVOLUTION MODEL 33
2.2.2 INTERACTING MULTIPLE MODELS 34
2.3 SENSOR LIKELIHOOD FUNCTIONS 35
2.3.1 GAUBIAN LIKELIHOOD FUNCTIONS 36
2.3.2 MULTIPLE SENSOR LIKELIHOOD 41
2.3.3 LIKELIHOOD FOR AMBIGUOUS DATA 44
HTTP://D-NB.INFO/1035341727
X
,
V
CONTENTS
2.3.4 INCORPORATING SIGNAL STRENGTH 48
2.3.5 EXTENDED OBJECT LIKELIHOOD 50
REFERENCES 51
3 BAYESIAN KNOWLEDGE PROPAGATION 53
3.1 BAYESIAN TRACKING PARADIGM 53
3.1.1 CHARACTERISTIC ASPECTS 56
3.1.2 REMARKS ON APPROXIMATIONS 57
3.1.3 ON TRACK-TO-TRACK FUSION 59
3.1.4 A FIRST LOOK AT INITIATION 60
3.2 OBJECT STATE PREDICTION 60
3.2.1 KALMAN PREDICTION 61
3.2.2 EXPECTATION GATES 61
3.2.3 IMM PREDICTION 63
3.3 DATA UPDATE: FILTERING 64
3.3.1 KALMAN FILTERING 64
3.3.2 IMM FILTERING 65
3.3.3 MHT FILTERING 66
3.4 OBJECT STATE RETRODICTION 70
3.4.1 FIXED INTERVAL SMOOTHING 72
3.4.2 CONTINUOUS TIME RETRODICTION 73
3.4.3 IMM RETRODICTION 74
3.4.4 MHT RETRODICTION 76
3.4.5 DISCUSSION OF AN EXAMPLE 78
REFERENCES 80
4 SEQUENTIAL TRACK EXTRACTION 83
4.1 WELL-SEPARATED OBJECTS 83
4.1.1 SEQUENTIAL LIKELIHOOD RATIO TEST 84
4.1.2 PROPERTIES RELEVANT TO TRACKING 84
4.1.3 RELATION TO MHT TRACKING 85
4.2 OBJECT CLUSTERS 86
4.2.1 GENERALIZED LIKELIHOOD RATIO 86
4.2.2 ON CLUSTER CARDINALITY 87
REFERENCES 87
5 ON RECURSIVE BATCH PROCESSING 89
5.1 ACCUMULATED OBJECT STATE DENSITIES 89
5.1.1 CLOSED-FORM REPRESENTATIONS 90
5.1.2 DISCUSSION OF GENERALIZATIONS 93
5.1.3 OUT-OF-SEQUENCE MEASUREMENTS 95
5.1.4 DISCUSSION OF AN EXAMPLE 96
5.2 SOLVING TRACKING PROBLEMS BY EM 98
5.2.1 DATA AUGMENTATION METHODS 99
CONTENTS *
V
5.2.2 EXPECTATION AND MAXIMIZATION STEPS 101
5.2.3 DISCUSSION OF PROBLEMS 104
REFERENCES 104
6 ASPECTS OF TRACK-TO-TRACK FUSION 107
6.1 FULL-RATE COMMUNICATION FUSION 108
6.1.1 FRENKEL TRACKLETS 108
6.1.2 TRACKLET FUSION 109
6.2 ARBITRARY COMMUNICATION RATE FUSION 109
6.2.1 GAUBIAN PRODUCT DENSITIES ILL
6.2.2 DISTRIBUTED FILTERING UPDATE 112
6.2.3 DISTRIBUTED PREDICTION UPDATE 113
6.2.4 DISTRIBUTED RETRODICTION UPDATE 116
6.3 DISCUSSION OF A SIMULATED EXAMPLE 119
REFERENCES 123
PART II SENSOR DATA FUSION: SELECTED APPLICATIONS
7 INTEGRATION OF ADVANCED SENSOR PROPERTIES 127
7.1 FINITE SENSOR RESOLUTION 127
7.1.1 A RADAR RESOLUTION MODEL 128
7.1.2 RESOLUTION-SPECIFIC LIKELIHOOD 131
7.1.3 A FORMATION TRACKING EXAMPLE 134
7.1.4 RESOLUTION: SUMMARY OF RESULTS 135
7.2 GMTI RADAR: DOPPLER BLINDNESS 137
7.2.1 AIR-TO-GROUND SURVEILLANCE 137
7.2.2 A MODEL FOR DOPPLER BLINDNESS 138
7.2.3 ESSENTIALS OF GMTI TRACKING 143
7.2.4 EFFECT OF GMTI-MODELING 144
7.3 MAIN-LOBE JAMMING 149
7.3.1 MODELING THE JAMMER NOTCH 149
7.3.2 TRACKING FILTER ALTERNATIVES 151
7.3.3 SELECTED SIMULATION RESULTS 152
7.4 NEGATIVE SENSOR INFORMATION 153
7.4.1 A UBIQUITOUS NOTION 153
7.4.2 LESSONS LEARNED FROM EXAMPLES 154
REFERENCES 155
8 INTEGRATION OF ADVANCED OBJECT PROPERTIES 157
8.1 MODEL HISTORY MHT TRACKING 158
8.1.1 IMM-MHT TRACKING 158
8.1.2 PERFORMANCE EVALUATION 160
8.1.3 IMM-MHT: CONCLUSIONS 166
XV
J
CONTENTS
8.2 EXTENDED OBJECT TRACKING 168
8.2.1 GENERALIZED FORMALISM 169
8.2.2 EXTENDED OBJECT PREDICTION 171
8.2.3 EXTENDED OBJECT FILTERING 173
8.2.4 EXTENDED OBJECT KINEMATICS 176
8.2.5 SELECTED SIMULATION RESULTS 177
8.2.6 SUMMARY OF RESULTS 180
8.3 TRACKING AND CLASSIFICATION 183
REFERENCES 184
9 INTEGRATION OF TOPOGRAPHICAL INFORMATION 187
9.1 ROAD-MAP ASSISTED TRACKING 188
9.1.1 MODELING OF ROAD SEGMENTS 189
9.1.2 ROAD-CONSTRAINED DENSITIES 190
9.1.3 QUANTITATIVE DISCUSSION 193
9.2 TRACK-BASED ROAD-MAP EXTRACTION 199
9.2.1 PRACTICAL RELEVANCE 199
9.2.2 ROAD NODE RECONSTRUCTION 200
9.2.3 DISCUSSION OF AN EXAMPLE 201
9.2.4 SUMMARY OF OBSERVATIONS 205
9.3 INTEGRATION OF RAY TRACERS 207
9.3.1 MULTIPATH PROPAGATION PREDICTION 207
9.3.2 PARTICLE FILTER APPROACH 208
9.3.3 CONCLUSION 208
REFERENCES 210
10 FEED-BACK TO ACQUISITION: SENSOR MANAGEMENT 211
10.1 INFORMATION FLOW IN AGILE-BEAM RADAR 212
10.2 SENSOR MODELING FOR PHASED-ARRAY RADAR 214
10.2.1 RADAR CROSS SECTION FLUCTUATIONS 214
10.2.2 MEAN RECEIVED SIGNAL-TO-NOISE RATIO 215
10.2.3 DETECTION AND MEASUREMENT PROCESS 216
10.3 BAYESIAN TRACKING ALGORITHMS REVISITED 217
10.3.1 PREDICTIONS: BASIS FOR ALLOCATION DECISIONS 217
10.3.2 PROCESSING OF SIGNAL STRENGTH INFORMATION 217
10.4 ADAPTIVE BAYESIAN SENSOR MANAGEMENT 219
10.4.1 ADAPTIVE RADAR REVISIT TIME CONTROL 219
10.4.2 TRANSMITTED RADAR ENERGY SELECTION 220
10.4.3 BAYESIAN LOCAL SEARCH PROCEDURES 220
10.5 DISCUSSION OF NUMERICAL SIMULATION RESULTS 223
10.5.1 DISCUSSION OF SIMULATION SCENARIOS 223
10.5.2 REMARKS ON IMM MODELING DESIGN 226
10.5.3 GAIN BY IMM MODELLING 227
10.5.4 ON THE QUALITY OF RCS ESTIMATES 230
CONTENTS XVII
10.5.5 RCS MODEL MISMATCH 230
10.5.6 ADAPTIVE ENERGY MANAGEMENT 231
10.6 ADAPTIVE SENSOR MANAGEMENT: SUMMARY
OF RESULTS 233
REFERENCES 234
APPENDIX A 237
Tracking and Sensor Data Fusion
Methodological Framework and Selected Applications
Sensor D|ata
Fusion
is the process of combining incomplete and imperfect pieces of
mutually complementary sensor information in such a way that a better understanding
of an underlying real-world phenomenon is achieved. Typically, this insight is either
unobtainable otherwise or a fusion result exceeds what can be produced from a single
sensor output in accuracy, reliability, or cost. This book provides an introduction Sensor
Data Fusion, as an information technology as well as a branch of engineering science and
informatics. Part
Î
presents a coherent methodological framework, thus providing the
prerequisites for discussing selected applications in Part II of the book. The presentation
mirrors the author's views on the subject and emphasizes his own contributions to the
development of particular aspects.
With some delay, Sensor Data Fusion is likely to develop along lines similar to the
evolution of another modern key technology whose origin is in the military domain, the
Internet. It is the author'
s
firm conviction that until now, scientists and engineers have
only scratched the surface of the vast range of opportunities for research, engineering,
and product development that still waits to be explored; the Internet of the Sensors. |
| any_adam_object | 1 |
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| id | DE-604.BV041452046 |
| illustrated | Illustrated |
| indexdate | 2025-02-20T07:17:53Z |
| institution | BVB |
| isbn | 9783642392719 9783642392702 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026898599 |
| oclc_num | 864484832 |
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| physical | XVII, 253 S. Ill., graph. Darst. 235 mm x 155 mm |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Springer |
| record_format | marc |
| series2 | Mathematical engineering |
| spellingShingle | Koch, Wolfgang Tracking and sensor data fusion methodological framework and selected applications Sensorsystem (DE-588)4307964-7 gnd Objektverfolgung (DE-588)4311226-2 gnd Datenfusion (DE-588)4582612-2 gnd Kontextbezogenes System (DE-588)4739720-2 gnd |
| subject_GND | (DE-588)4307964-7 (DE-588)4311226-2 (DE-588)4582612-2 (DE-588)4739720-2 |
| title | Tracking and sensor data fusion methodological framework and selected applications |
| title_auth | Tracking and sensor data fusion methodological framework and selected applications |
| title_exact_search | Tracking and sensor data fusion methodological framework and selected applications |
| title_full | Tracking and sensor data fusion methodological framework and selected applications Wolfgang Koch |
| title_fullStr | Tracking and sensor data fusion methodological framework and selected applications Wolfgang Koch |
| title_full_unstemmed | Tracking and sensor data fusion methodological framework and selected applications Wolfgang Koch |
| title_short | Tracking and sensor data fusion |
| title_sort | tracking and sensor data fusion methodological framework and selected applications |
| title_sub | methodological framework and selected applications |
| topic | Sensorsystem (DE-588)4307964-7 gnd Objektverfolgung (DE-588)4311226-2 gnd Datenfusion (DE-588)4582612-2 gnd Kontextbezogenes System (DE-588)4739720-2 gnd |
| topic_facet | Sensorsystem Objektverfolgung Datenfusion Kontextbezogenes System |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=4352639&prov=M&dok%5Fvar=1&dok%5Fext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026898599&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026898599&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kochwolfgang trackingandsensordatafusionmethodologicalframeworkandselectedapplications |
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THWS Würzburg Zentralbibliothek Lesesaal
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