<div class="csl-bib-body">
<div class="csl-entry">Ashury, M., Gerstoft, P., Mecklenbräuker, C. F., & Lungenschmied, D. (2023). Channel Estimation for FMCW Radar with Sparse Bayesian Learning. In <i>Proceedings 2023 IEEE Conference on Antenna Measurements and Applications 2023 IEEE Conference on Antenna Measurements and Applications (CAMA)</i> (pp. 266–270). https://doi.org/10.1109/CAMA57522.2023.10352684</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/192230
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dc.description.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.
en
dc.language.iso
en
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dc.subject
wireless channel
en
dc.subject
Automotive sensors
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dc.subject
sparse Bayesian learning
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dc.title
Channel Estimation for FMCW Radar with Sparse Bayesian Learning
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
University of California, San Diego, United States of America (the)
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dc.contributor.affiliation
Infineon Technologies (Austria), Austria
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dc.relation.isbn
979-8-3503-2304-7
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dc.description.startpage
266
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dc.description.endpage
270
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Proceedings 2023 IEEE Conference on Antenna Measurements and Applications 2023 IEEE Conference on Antenna Measurements and Applications (CAMA)
