Details
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE Radar Conference, RadarConf 2020 |
| Publisher | IEEE Computer Society |
| ISBN (electronic) | 9781728189420 |
| ISBN (print) | 978-1-7281-8942-0 |
| Publication status | Published - 2020 |
| Event | 2020 IEEE Radar Conference - online Duration: 21 Sept 2020 → 25 Sept 2020 https://www.radarconf20.org/ |
Publication series
| Name | IEEE National Radar Conference - Proceedings |
|---|---|
| Volume | 2020-September |
| ISSN (Print) | 1097-5659 |
Abstract
Keywords
- backprojection, autofocus, SAR, GPU, CUDA
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
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2020 IEEE Radar Conference, RadarConf 2020. IEEE Computer Society, 2020. 9266636 (IEEE National Radar Conference - Proceedings; Vol. 2020-September).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Optimized Minimum-Search for SAR Backprojection Autofocus on GPUs Using CUDA
AU - Rother, Niklas
AU - Fahnemann, Christian
AU - Wittler, Jan
AU - Blume, Holger Christoph
PY - 2020
Y1 - 2020
N2 - Autofocus techniques for synthetic aperture radar (SAR) can improve the image quality substantially. Their high computational complexity imposes a challenge when employing them in runtime-critical implementations. This paper presents an autofocus implementation for stripmap SAR specially optimized for parallel architectures like GPUs. Thorough evaluation using real SAR data shows that the tunable parameters of the algorithm allow to counterbalance runtime and achieved image quality.
AB - Autofocus techniques for synthetic aperture radar (SAR) can improve the image quality substantially. Their high computational complexity imposes a challenge when employing them in runtime-critical implementations. This paper presents an autofocus implementation for stripmap SAR specially optimized for parallel architectures like GPUs. Thorough evaluation using real SAR data shows that the tunable parameters of the algorithm allow to counterbalance runtime and achieved image quality.
KW - backprojection
KW - autofocus
KW - SAR
KW - GPU
KW - CUDA
UR - http://www.scopus.com/inward/record.url?scp=85098570561&partnerID=8YFLogxK
U2 - 10.15488/13271
DO - 10.15488/13271
M3 - Conference contribution
SN - 978-1-7281-8942-0
T3 - IEEE National Radar Conference - Proceedings
BT - 2020 IEEE Radar Conference, RadarConf 2020
PB - IEEE Computer Society
T2 - 2020 IEEE Radar Conference
Y2 - 21 September 2020 through 25 September 2020
ER -