TY - GEN
T1 - Resource-Constrained Optimizations For Synthetic Aperture Radar On-Board Image Processing
AU - Schlemon, Maron
AU - Schulz, Martin
AU - Scheiber, Rolf
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Synthetic Aperture Radar (SAR) can be used to create realistic and high-resolution 2D or 3D reconstructions of landscapes. The data capture is typically deployed using radar instruments in specially equipped, low flying planes, resulting in a large amount of raw data, which needs to be processed for image reconstruction. However, due to limited on-board processing capacities on the plane (power, size, weight, cooling, communication bandwidth to ground stations, etc.) and the need to capture many images during a single flight, the raw data must be processed on-board and then sent to the ground station efficiently as image products. In this paper we describe the processing architecture of the digital beamforming SAR (DBFSAR) of the German Areaospace Center (DLR) and the special steps that had to be taken to enable the on-board processing. We explain the required software optimizations and under which conditions their integration in the SAR imaging process leads to (near) real-time capability. We further describe the lessons learned in our work and discuss how they can be applied to other processing scenarios with limited resource availability.
AB - Synthetic Aperture Radar (SAR) can be used to create realistic and high-resolution 2D or 3D reconstructions of landscapes. The data capture is typically deployed using radar instruments in specially equipped, low flying planes, resulting in a large amount of raw data, which needs to be processed for image reconstruction. However, due to limited on-board processing capacities on the plane (power, size, weight, cooling, communication bandwidth to ground stations, etc.) and the need to capture many images during a single flight, the raw data must be processed on-board and then sent to the ground station efficiently as image products. In this paper we describe the processing architecture of the digital beamforming SAR (DBFSAR) of the German Areaospace Center (DLR) and the special steps that had to be taken to enable the on-board processing. We explain the required software optimizations and under which conditions their integration in the SAR imaging process leads to (near) real-time capability. We further describe the lessons learned in our work and discuss how they can be applied to other processing scenarios with limited resource availability.
KW - High Performance Algorithms
KW - On-Board Radar Processing
KW - Resource Constrained Processing
KW - Synthetic Aperture Radar
UR - http://www.scopus.com/inward/record.url?scp=85142240205&partnerID=8YFLogxK
U2 - 10.1109/HPEC55821.2022.9926327
DO - 10.1109/HPEC55821.2022.9926327
M3 - Conference contribution
AN - SCOPUS:85142240205
T3 - 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
BT - 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
Y2 - 19 September 2022 through 23 September 2022
ER -