Radar for fully autonomous driving:
Gespeichert in:
Weitere Verfasser: | |
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Boston ; London
Artech House
[2022]
|
Online-Zugang: | FHD01 FHI01 TUM01 TUM01 Volltext Volltext |
Beschreibung: | 1 Online-Ressource (xix, 311 Seiten) |
ISBN: | 9781523145768 9781630818975 |
Internformat
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505 | 8 | |a Intro -- Radar for Fully Autonomous Driving -- Contents -- Part I: Radar Technologies for Autonomous Vehicles -- Chapter 1 Modern Radar Sensors in Advanced Automotive Architectures -- 1.1 Inspiration for More Advanced Systems -- 1.1.1 Traffic Density and Fatal Accident Rate -- 1.1.2 Human Factor -- 1.1.3 Autonomous Driving Levels -- 1.2 The Evolving Automotive Radar Landscape -- 1.3 Fast Chirp Sequence Radar Sensing -- 1.4 RFCMOS Car Radar Transceiver -- 1.5 Elements of a Radar Module -- 1.6 Angular Resolution Increase: MIMO Example and Cascaded Application | |
505 | 8 | |a 1.7 Vehicle Network and Compute Considerations -- 1.7.1 Vehicle Network Architecture Evolution -- 1.7.2 Distributed Versus Centralized Processing -- 1.7.3 Conclusion -- 1.8 Summary -- 1.9 Acknowledgments -- References -- Chapter 2 Design Considerations for Automotive Radar -- 2.1 Radar Requirements -- 2.2 The Spectrum for Automotive Radar -- 2.3 Range (Distance) Required for Automotive Radar -- 2.4 Automotive Radar Installation -- 2.5 Automotive Radar Considerations for Scanning the FOV -- 2.6 Frequency Modulation Waveforms and the Radar Data Cube -- 2.7 Outputs from Automotive Radar | |
505 | 8 | |a 4.1 Virtual Array Synthesis via MIMO Radar -- 4.2 Waveform Orthogonality Strategies in Automotive MIMO Radar -- 4.2.1 Waveform Orthogonality via TDM -- 4.2.2 Waveform Orthogonality via DDM -- 4.2.3 Waveform Orthogonality via FDM -- 4.3 Angle Finding in Automotive MIMO Radar -- 4.3.1 High Resolution Angle Finding with ULA -- 4.3.2 High Resolution Angle Finding with SLA -- 4.4 High Resolution Imaging Radar for Autonomous Driving -- 4.4.1 Cascade of Multiple Radar Transceivers -- 4.4.2 Examples of Cascaded Imaging Radars -- 4.4.3 Design Challenges of Imaging Radar | |
505 | 8 | |a 4.5 Challenges in Automotive MIMO Radar -- 4.5.1 Angle Finding in the Presence of Multipath Reflections -- 4.5.2 Waveform Orthogonality in Automotive MIMO Radar -- 4.5.3 Efficient, High Resolution Angle Finding Algorithms Are Needed -- References -- Chapter 5 Synthetic Aperture Radar for Automotive Applications -- 5.1 Introduction -- 5.1.1 Historical Background -- 5.1.2 Comparison to Traditional Radar Systems -- 5.1.3 SAR and Point Cloud Imaging Performance -- 5.1.4 Applications for Automotive Use -- 5.2 Mathematical Foundation -- 5.2.1 Key Assumptions -- 5.2.2 Signal Model -- 5.2.3 Slow Time | |
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Datensatz im Suchindex
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adam_txt | |
any_adam_object | |
any_adam_object_boolean | |
author2 | Markel, Matt |
author2_role | edt |
author2_variant | m m mm |
author_facet | Markel, Matt |
building | Verbundindex |
bvnumber | BV048277765 |
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contents | Intro -- Radar for Fully Autonomous Driving -- Contents -- Part I: Radar Technologies for Autonomous Vehicles -- Chapter 1 Modern Radar Sensors in Advanced Automotive Architectures -- 1.1 Inspiration for More Advanced Systems -- 1.1.1 Traffic Density and Fatal Accident Rate -- 1.1.2 Human Factor -- 1.1.3 Autonomous Driving Levels -- 1.2 The Evolving Automotive Radar Landscape -- 1.3 Fast Chirp Sequence Radar Sensing -- 1.4 RFCMOS Car Radar Transceiver -- 1.5 Elements of a Radar Module -- 1.6 Angular Resolution Increase: MIMO Example and Cascaded Application 1.7 Vehicle Network and Compute Considerations -- 1.7.1 Vehicle Network Architecture Evolution -- 1.7.2 Distributed Versus Centralized Processing -- 1.7.3 Conclusion -- 1.8 Summary -- 1.9 Acknowledgments -- References -- Chapter 2 Design Considerations for Automotive Radar -- 2.1 Radar Requirements -- 2.2 The Spectrum for Automotive Radar -- 2.3 Range (Distance) Required for Automotive Radar -- 2.4 Automotive Radar Installation -- 2.5 Automotive Radar Considerations for Scanning the FOV -- 2.6 Frequency Modulation Waveforms and the Radar Data Cube -- 2.7 Outputs from Automotive Radar 4.1 Virtual Array Synthesis via MIMO Radar -- 4.2 Waveform Orthogonality Strategies in Automotive MIMO Radar -- 4.2.1 Waveform Orthogonality via TDM -- 4.2.2 Waveform Orthogonality via DDM -- 4.2.3 Waveform Orthogonality via FDM -- 4.3 Angle Finding in Automotive MIMO Radar -- 4.3.1 High Resolution Angle Finding with ULA -- 4.3.2 High Resolution Angle Finding with SLA -- 4.4 High Resolution Imaging Radar for Autonomous Driving -- 4.4.1 Cascade of Multiple Radar Transceivers -- 4.4.2 Examples of Cascaded Imaging Radars -- 4.4.3 Design Challenges of Imaging Radar 4.5 Challenges in Automotive MIMO Radar -- 4.5.1 Angle Finding in the Presence of Multipath Reflections -- 4.5.2 Waveform Orthogonality in Automotive MIMO Radar -- 4.5.3 Efficient, High Resolution Angle Finding Algorithms Are Needed -- References -- Chapter 5 Synthetic Aperture Radar for Automotive Applications -- 5.1 Introduction -- 5.1.1 Historical Background -- 5.1.2 Comparison to Traditional Radar Systems -- 5.1.3 SAR and Point Cloud Imaging Performance -- 5.1.4 Applications for Automotive Use -- 5.2 Mathematical Foundation -- 5.2.1 Key Assumptions -- 5.2.2 Signal Model -- 5.2.3 Slow Time |
ctrlnum | (OCoLC)1334056114 (DE-599)BVBBV048277765 |
format | Electronic eBook |
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id | DE-604.BV048277765 |
illustrated | Not Illustrated |
index_date | 2024-07-03T20:00:38Z |
indexdate | 2024-07-10T09:33:58Z |
institution | BVB |
isbn | 9781523145768 9781630818975 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033657941 |
oclc_num | 1334056114 |
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owner | DE-1050 DE-91G DE-BY-TUM DE-573 |
owner_facet | DE-1050 DE-91G DE-BY-TUM DE-573 |
physical | 1 Online-Ressource (xix, 311 Seiten) |
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publishDate | 2022 |
publishDateSearch | 2022 |
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publisher | Artech House |
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spelling | Radar for fully autonomous driving Matt Markel, editor Boston ; London Artech House [2022] 1 Online-Ressource (xix, 311 Seiten) txt rdacontent c rdamedia cr rdacarrier Intro -- Radar for Fully Autonomous Driving -- Contents -- Part I: Radar Technologies for Autonomous Vehicles -- Chapter 1 Modern Radar Sensors in Advanced Automotive Architectures -- 1.1 Inspiration for More Advanced Systems -- 1.1.1 Traffic Density and Fatal Accident Rate -- 1.1.2 Human Factor -- 1.1.3 Autonomous Driving Levels -- 1.2 The Evolving Automotive Radar Landscape -- 1.3 Fast Chirp Sequence Radar Sensing -- 1.4 RFCMOS Car Radar Transceiver -- 1.5 Elements of a Radar Module -- 1.6 Angular Resolution Increase: MIMO Example and Cascaded Application 1.7 Vehicle Network and Compute Considerations -- 1.7.1 Vehicle Network Architecture Evolution -- 1.7.2 Distributed Versus Centralized Processing -- 1.7.3 Conclusion -- 1.8 Summary -- 1.9 Acknowledgments -- References -- Chapter 2 Design Considerations for Automotive Radar -- 2.1 Radar Requirements -- 2.2 The Spectrum for Automotive Radar -- 2.3 Range (Distance) Required for Automotive Radar -- 2.4 Automotive Radar Installation -- 2.5 Automotive Radar Considerations for Scanning the FOV -- 2.6 Frequency Modulation Waveforms and the Radar Data Cube -- 2.7 Outputs from Automotive Radar 4.1 Virtual Array Synthesis via MIMO Radar -- 4.2 Waveform Orthogonality Strategies in Automotive MIMO Radar -- 4.2.1 Waveform Orthogonality via TDM -- 4.2.2 Waveform Orthogonality via DDM -- 4.2.3 Waveform Orthogonality via FDM -- 4.3 Angle Finding in Automotive MIMO Radar -- 4.3.1 High Resolution Angle Finding with ULA -- 4.3.2 High Resolution Angle Finding with SLA -- 4.4 High Resolution Imaging Radar for Autonomous Driving -- 4.4.1 Cascade of Multiple Radar Transceivers -- 4.4.2 Examples of Cascaded Imaging Radars -- 4.4.3 Design Challenges of Imaging Radar 4.5 Challenges in Automotive MIMO Radar -- 4.5.1 Angle Finding in the Presence of Multipath Reflections -- 4.5.2 Waveform Orthogonality in Automotive MIMO Radar -- 4.5.3 Efficient, High Resolution Angle Finding Algorithms Are Needed -- References -- Chapter 5 Synthetic Aperture Radar for Automotive Applications -- 5.1 Introduction -- 5.1.1 Historical Background -- 5.1.2 Comparison to Traditional Radar Systems -- 5.1.3 SAR and Point Cloud Imaging Performance -- 5.1.4 Applications for Automotive Use -- 5.2 Mathematical Foundation -- 5.2.1 Key Assumptions -- 5.2.2 Signal Model -- 5.2.3 Slow Time Markel, Matt edt Erscheint auch als Druck-Ausgabe 978-1-63081-896-8 https://ebs-patron.eb20.com/AccessTitle/ISBN/9781630818975 Verlag URL des Erstveröffentlichers Volltext https://ieeexplore.ieee.org/document/9826867 Aggregator URL des Erstveröffentlichers Volltext |
spellingShingle | Radar for fully autonomous driving Intro -- Radar for Fully Autonomous Driving -- Contents -- Part I: Radar Technologies for Autonomous Vehicles -- Chapter 1 Modern Radar Sensors in Advanced Automotive Architectures -- 1.1 Inspiration for More Advanced Systems -- 1.1.1 Traffic Density and Fatal Accident Rate -- 1.1.2 Human Factor -- 1.1.3 Autonomous Driving Levels -- 1.2 The Evolving Automotive Radar Landscape -- 1.3 Fast Chirp Sequence Radar Sensing -- 1.4 RFCMOS Car Radar Transceiver -- 1.5 Elements of a Radar Module -- 1.6 Angular Resolution Increase: MIMO Example and Cascaded Application 1.7 Vehicle Network and Compute Considerations -- 1.7.1 Vehicle Network Architecture Evolution -- 1.7.2 Distributed Versus Centralized Processing -- 1.7.3 Conclusion -- 1.8 Summary -- 1.9 Acknowledgments -- References -- Chapter 2 Design Considerations for Automotive Radar -- 2.1 Radar Requirements -- 2.2 The Spectrum for Automotive Radar -- 2.3 Range (Distance) Required for Automotive Radar -- 2.4 Automotive Radar Installation -- 2.5 Automotive Radar Considerations for Scanning the FOV -- 2.6 Frequency Modulation Waveforms and the Radar Data Cube -- 2.7 Outputs from Automotive Radar 4.1 Virtual Array Synthesis via MIMO Radar -- 4.2 Waveform Orthogonality Strategies in Automotive MIMO Radar -- 4.2.1 Waveform Orthogonality via TDM -- 4.2.2 Waveform Orthogonality via DDM -- 4.2.3 Waveform Orthogonality via FDM -- 4.3 Angle Finding in Automotive MIMO Radar -- 4.3.1 High Resolution Angle Finding with ULA -- 4.3.2 High Resolution Angle Finding with SLA -- 4.4 High Resolution Imaging Radar for Autonomous Driving -- 4.4.1 Cascade of Multiple Radar Transceivers -- 4.4.2 Examples of Cascaded Imaging Radars -- 4.4.3 Design Challenges of Imaging Radar 4.5 Challenges in Automotive MIMO Radar -- 4.5.1 Angle Finding in the Presence of Multipath Reflections -- 4.5.2 Waveform Orthogonality in Automotive MIMO Radar -- 4.5.3 Efficient, High Resolution Angle Finding Algorithms Are Needed -- References -- Chapter 5 Synthetic Aperture Radar for Automotive Applications -- 5.1 Introduction -- 5.1.1 Historical Background -- 5.1.2 Comparison to Traditional Radar Systems -- 5.1.3 SAR and Point Cloud Imaging Performance -- 5.1.4 Applications for Automotive Use -- 5.2 Mathematical Foundation -- 5.2.1 Key Assumptions -- 5.2.2 Signal Model -- 5.2.3 Slow Time |
title | Radar for fully autonomous driving |
title_auth | Radar for fully autonomous driving |
title_exact_search | Radar for fully autonomous driving |
title_exact_search_txtP | Radar for fully autonomous driving |
title_full | Radar for fully autonomous driving Matt Markel, editor |
title_fullStr | Radar for fully autonomous driving Matt Markel, editor |
title_full_unstemmed | Radar for fully autonomous driving Matt Markel, editor |
title_short | Radar for fully autonomous driving |
title_sort | radar for fully autonomous driving |
url | https://ebs-patron.eb20.com/AccessTitle/ISBN/9781630818975 https://ieeexplore.ieee.org/document/9826867 |
work_keys_str_mv | AT markelmatt radarforfullyautonomousdriving |