Tracking and radar sensor modelling for automotive safety systems:
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Göteborg
Chalmers Univ. of Technology, Dept. of Signals and Systems
2010
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Göteborg, Univ., Diss. |
| Physical Description: | XII, 279 S. |
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MARC
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| 001 | BV036123874 | ||
| 003 | DE-604 | ||
| 005 | 20100420 | ||
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| 084 | |a ZN 6500 |0 (DE-625)157567: |2 rvk | ||
| 100 | 1 | |a Danielsson, Lars |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Tracking and radar sensor modelling for automotive safety systems |c by Lars Danielsson |
| 264 | 1 | |a Göteborg |b Chalmers Univ. of Technology, Dept. of Signals and Systems |c 2010 | |
| 300 | |a XII, 279 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Göteborg, Univ., Diss. | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 856 | 4 | 2 | |m Digitalisierung UB Passau |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=019013773&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-019013773 | ||
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| _version_ | 1804141232213983232 |
|---|---|
| adam_text | Contents
Abstract
і
Acknowledgments
iii
List of publications
v
Contents
vii
Part I: Introduction
1
1
Introduction
3
1.1
Thesis outline and contributions
................. 5
1.2
Future work
............................ 9
2
Preventive safety systems
11
2.1
Background of in-vehicle safety systems
............. 11
2.1.1
Protective safety systems
................. 12
2.1.2
Preventive safety systems
................ 12
2.1.3
Integrated safety systems
................. 15
2.2
Motivation and safety benefit
.................. 15
2.3
Basic system components
..................... 16
2.3.1
Perception layer
...................... 17
2.3.2
Decision layer
....................... 18
2.3.3
Action layer
........................ 19
2.4
Summary
............................. 20
3
Bayesian tracking theory
23
3.1
Recursive filtering problem
.................... 26
3.1.1
General
recursive
filtering
problem
........... 26
3.1.2
Kalman Filter
....................... 29
3.1.3
The extended
Kalman filter
............... 34
3.1.4
Unscented Kalman Filter
................. 39
3.1.5
Particle
Filters
...................... 47
3.2
Models
............................... 52
3.2.1
Motion models
...................... 53
3.2.2
Sensor
models
....................... 57
3.3
Data association
......................... 59
3.3.1
Gating
........................... 61
3.3.2
Nearest neighbour association
.............. 63
3.3.3
All-neighbour association
................. 64
3.4
Track management
........................ 66
3.4.1
Track initialisation
.................... 68
3.4.2
Track validation
...................... 68
3.4.3
Track deletion
....................... 69
4
Radar sensor modelling
71
4.1
Brief history of radar and its applications
............ 71
4.2
Radar system description
..................... 72
4.3
Basic radar measurement principles
............... 74
4.3.1
Range measurement
................... 75
4.3.2
Angular measurement
.................. 78
4.3.3
Range rate measurement
................. 80
4.4
Signal model
............................ 84
4.4.1
Transmit signal
...................... 84
4.4.2
Radar equation
...................... 86
4.4.3
Object return model
................... 87
4.4.4
Receiver and matched filtering
.............. 90
4.4.5
Detection
......................... 92
Bibliography
95
Part II: Publications
103
Paper I: A radar sensor model for improved tracking of vehicles
using possibly unresolved detections
107
Abstract
.................................107
1
Introduction
............................107
2
Problem formulation
.......................
Ill
2.1
State parametrization
...................
Ill
2.2
Radar
observations....................
112
2.3
Objectives
.........................114
3
Radar sensor model
.......................115
3.1
Reflection center model
..................116
3.2
Cluster model
.......................118
3.3
Group model
.......................120
3.4
Target measurement model
...............123
4
Tracking framework
........................123
4.1
State prediction
......................125
4.2
Measurement prediction
.................126
4.3
Data association
.....................128
4.4
Measurement update
...................130
5
Evaluation
.............................131
5.1
Point source model
....................132
5.2
Sensor model comparison
.................132
5.3
Tracking filter comparison
................134
6
Conclusion
.............................134
A Reflector mapping
.........................135
A.I Reflector position
.....................135
A.
2
Signal amplitude
.....................136
В
Gaussian cluster density approximation
.............136
B.I Mean approximation
...................137
B.2 Covariance approximation
................137
Paper II: Multitarget sensor resolution model for arbitrary tar¬
get numbers
145
Abstract
.................................145
1
Introduction
............................145
2
Problem formulation
.......................146
3
N-target sensor resolution model
.................148
3.1
Resolution event probabilities
..............149
3.2
Pattern likelihood
.....................152
4
Calculation of the posterior density
...............153
4.1
Update with the likelihood function
...........154
4.2
Update with the data association probabilities
.....155
4.3
Update with the resolution probabilities
........156
4.4
Summary
.........................159
5
Feasibility
.............................161
5.1
JPDA approximation
...................161
5.2
Further approximations
.................162
6
Conclusions
............................162
Paper
III: Adaptive
radar
sensor model for tracking structured
extended objects
167
1
Introduction
............................ 167
2
Problem formulation and modelling assumptions
........ 170
2.1
Extended object model
.................. 170
2.2
Radar observations
.................... 172
2.3
Tracking problem
..................... 173
3
Structure model
.......................... 174
3.1
Structure parametrisation
................ 174
3.2
Structure process model
................. 176
3.3
Feature process model
.................. 176
3.4
Extended object process model
............. 178
4
Radar sensor model
........................ 179
4.1
Reflector model
...................... 180
4.2
Cluster model
....................... 180
4.3
Sensor resolution model
................. 182
4.4
Summary
......................... 185
5
Posterior Density
......................... 186
5.1
Prior density
....................... 186
5.2
Hypothesis set
....................... 187
5.3
Derivation of posterior density
.............. 188
6
Tracking framework
........................ 189
6.1
Measurement update mixture
.............. 190
6.2
Resolution model mixture
................ 193
6.3
Existence model mixture
................. 197
6.4
Merge and prune
..................... 197
7
Evaluation
............................. 199
7.1
The evaluation setting
.................. 199
7.2
Tracking filter comparison
................ 201
8
Conclusion
............................. 204
A Reflector mapping
......................... 204
A.I Reflector model
...................... 204
A.
2
Signal amplitude
..................... 205
Paper IV: Road Mapping using Radar Measurements with a
Probability Hypothesis Density Filter
211
1
Introduction
............................ 211
2
Problem Formulation
....................... 213
2.1
Road Mapping
...................... 214
2.2
Probability
Hypothesis Density
.............215
2.3
Spawning of New Targets
................218
2.4
Pruning and Merging
...................218
3
PHD Filter
............................219
3.1
Time Evolution
......................219
3.2
Measurement Update
...................220
4
Joint Clustering and Estimation
.................220
4.1
К
-Means
.........................221
4.2
New Algorithm
......................221
4.3
Road Edge Estimation
..................223
5
Merging
..............................224
5.1
Algorithm
.........................226
5.2
Road Mapping
......................226
6
Application Example
.......................228
6.1
System Model
.......................230
6.2
Spawn Process
......................230
6.3
GM-PHD Filter Recursion
................231
6.4
Experiments and Results
.................233
7
Conclusion
.............................233
Paper V: A new vehicle motion model for improved predictions
and situation assessment
241
Abstract
.................................241
1
Introduction
............................241
2
Background
............................243
3
Motion model framework
.....................246
3.1
Model structure
......................246
3.2
Calculating the driver input uk(xk)
...........248
3.3
Model features
......................249
4
Designing cost functions
.....................250
4.1
Longitudinal cost function,
c¡o(·,·)
............251
4.2
Lateral cost function, cia(·,
■)...............251
4.3
Comfort cost function, cc(·,·)
..............252
4.4
Cost functions for vehicle interaction, Cjn(-,
·)......253
5
Evaluation results
.........................254
5.1
Evaluation criteria
....................255
5.2
Filtering 9k
........................256
5.3
Results
...........................259
6
Conclusion
.............................262
Paper
VI:
A design
architecture
for sensor data fusion systems
with application to automotive safety
268
|
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| author_facet | Danielsson, Lars |
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| author_sort | Danielsson, Lars |
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| building | Verbundindex |
| bvnumber | BV036123874 |
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| ctrlnum | (OCoLC)634471585 (DE-599)BVBBV036123874 |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
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| indexdate | 2024-07-09T22:12:36Z |
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| language | English |
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| physical | XII, 279 S. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Chalmers Univ. of Technology, Dept. of Signals and Systems |
| record_format | marc |
| spelling | Danielsson, Lars Verfasser aut Tracking and radar sensor modelling for automotive safety systems by Lars Danielsson Göteborg Chalmers Univ. of Technology, Dept. of Signals and Systems 2010 XII, 279 S. txt rdacontent n rdamedia nc rdacarrier Göteborg, Univ., Diss. (DE-588)4113937-9 Hochschulschrift gnd-content Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=019013773&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Danielsson, Lars Tracking and radar sensor modelling for automotive safety systems |
| subject_GND | (DE-588)4113937-9 |
| title | Tracking and radar sensor modelling for automotive safety systems |
| title_auth | Tracking and radar sensor modelling for automotive safety systems |
| title_exact_search | Tracking and radar sensor modelling for automotive safety systems |
| title_full | Tracking and radar sensor modelling for automotive safety systems by Lars Danielsson |
| title_fullStr | Tracking and radar sensor modelling for automotive safety systems by Lars Danielsson |
| title_full_unstemmed | Tracking and radar sensor modelling for automotive safety systems by Lars Danielsson |
| title_short | Tracking and radar sensor modelling for automotive safety systems |
| title_sort | tracking and radar sensor modelling for automotive safety systems |
| topic_facet | Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=019013773&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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