Deep Learning Methods for Automotive Radar Signal Processing.
| Kaituhi matua: | |
|---|---|
| Hōputu: | Licensed eBooks |
| Reo: | Ingarihi |
| I whakaputaina: |
Göttingen :
Cuvillier Verlag,
2021.
|
| Urunga tuihono: | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2983906 |
Rārangi ihirangi:
- Intro
- 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