- 136 pages
- English
- PDF
- Available on iOS & Android
About this book
Um autonomes Fahren zu ermƶglichen, mĆ¼ssen zukĆ¼nftige Sensorsysteme nicht nur in der Lage sein, das Fahrumfeld zu erfassen, sondern auch semantische Informationen zu liefern. In dieser Arbeit werden Deep Learning Methoden, die die klassische Radarsignalverarbeitungskette verbessern oder sogar ersetzen sollen, entwickelt und im Hinblick auf das Automobilumfeld evaluiert. FĆ¼r diesen Zweck werden hochmoderne Bilderkennungsalgorithmen auf die DomƤne der Radarsignale angepasst und zur Klassifizierung und Detektion verschiedener Verkehrsteilnehmer angewendet. For autonomous driving to become a reality, future sensor systems must be able to not only capture the vehicle's environment, but also to provide semantic information. In this work, deep learning methods, meant to enhanceāor even replaceāthe classical radar signal processing chain, are developed and evaluated in the context of automotive applications. For this purpose, state of the art computer vision approaches are adapted and applied to radar signals in order to detect and classify different road users.
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Information
Table of contents
- 1 Introduction
- 1.1 Goals and Contents of this Work
- 2 Radar Fundamentals
- 2.1 Continuous Wave Radar
- 2.2 Mono-Frequent Continuous Wave Radar
- 2.3 Linear Frequency Modulated Continuous WaveRadar
- 2.4 Chirp Sequence Frequency Modulated ContinuousWave Radar
- 2.5 Target Detection
- 2.6 Phased Arrays
- 2.7 Radar System Considerations
- 3 Machine Learning Fundamentals
- 3.1 Supervised Learning
- 3.2 Artificial Neural Networks
- 3.3 Training of Artificial Neural Networks
- 3.5 Loss Functions
- 3.6 Evaluation Metrics
- 4 Classification of Vulnerable RoadUsers
- 4.1 The Micro-Doppler Effect
- 4.2 Single Frame Vulnerable Road Users Classification
- 4.3 Joint Lidar and Radar Classification System
- 4.4 Concluding Remarks
- 5 Deep Learning Based Radar TargetDetection
- 5.1 Detection in Frequency Domain
- 5.2 Time Domain Detection
- 5.3 Concluding Remarks
- 6 Conclusion
- 6.1 Outlook
- Symbols
- Acronyms
- Bibliography
- Own Publications