Record link: 
http://hdl.handle.net/20.500.12708/192230
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Title: 
Channel Estimation for FMCW Radar with Sparse Bayesian Learning
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Citation: 
Ashury, M., Gerstoft, P., Mecklenbräuker, C. F., & Lungenschmied, D. (2023). Channel Estimation for FMCW Radar with Sparse Bayesian Learning. In Proceedings 2023 IEEE Conference on Antenna Measurements and Applications 2023 IEEE Conference on Antenna Measurements and Applications (CAMA) (pp. 266–270). https://doi.org/10.1109/CAMA57522.2023.10352684
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Publisher DOI: 
10.1109/CAMA57522.2023.10352684
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Ashury, Mehdi 
Gerstoft, Peter 
Mecklenbräuker, Christoph F. 
Lungenschmied, Dominik 
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Organisational Unit: 
E389-02 - Forschungsbereich Wireless Communications 
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Published in: 
Proceedings 2023 IEEE Conference on Antenna Measurements and Applications 2023 IEEE Conference on Antenna Measurements and Applications (CAMA) 
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ISBN: 
979-8-3503-2304-7
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Date (published): 
15-Nov-2023
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Event name: 
2023 IEEE Conference on Antenna Measurements and Applications (CAMA) 
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Event date: 
15-Nov-2023 - 17-Nov-2023
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Event place: 
Genova, Italy
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Number of Pages: 
5
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Keywords: 
wireless channel; Automotive sensors; sparse Bayesian learning
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Abstract: 
Automotive Millimeter-wave (mmWave) radar is an essential part of Advanced Driver Assistance Systems (ADAS), with fast-developing applications. Knowledge of the mmWave propagation channel characteristics is essential for designing a robust and reliable automotive radar system. In this contribution the Frequency Modulated Continuous-Wave (FMCW) Radar propagation channel is measured in laboratory conditions. Sparse Bayesian Learning (SBL) is used for post-processing of the obtained measurement data.
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Research Areas: 
Telecommunication: 50%
Sensor Systems: 50%
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Science Branch: 
2020 - Elektrotechnik, Elektronik, Informationstechnik: 100%
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Appears in Collections:
Conference Paper

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