Computation Reduction Oriented Circular Scanning SAR Raw Data Simulation on Multi-GPUs

HU Chen; ZHANG Fan; LI Guojun; LI Wei; CUI Zhongma

doi:10.12000/JR15078

Volume 5 Issue 4

Aug. 2016

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Article Navigation > Journal of Radars > 2016 > 5(4): 434-443

ZHANG Xuqi, ZHOU Bin, LIU Haiqi, et al. A scalable method for group target tracking using multisensor with limited field of views[J]. Journal of Radars, 2024, 13(6): 1220–1238. doi: 10.12000/JR24054

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HU Chen, ZHANG Fan, LI Guojun, LI Wei, CUI Zhongma. Computation Reduction Oriented Circular Scanning SAR Raw Data Simulation on Multi-GPUs[J]. Journal of Radars, 2016, 5(4): 434-443. doi: 10.12000/JR15078

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Computation Reduction Oriented Circular Scanning SAR Raw Data Simulation on Multi-GPUs

DOI: 10.12000/JR15078

1.
(College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China)
2.
(Beijing Remote Sensing Equipment Research Institute, Beijing 100854, China)

Funds:

The National Natural Science Foundation of China (61501018, 61302164), The Fundamental Research Funds for the Central Universities (YS1404), The Beijing Higher Education Young Elite Teacher Project (YETP0500)

Received Date: 2015-06-18
Rev Recd Date: 2015-09-14
Publish Date: 2016-08-28

Abstract

Abstract

As a special working mode, the circular scanning Synthetic Aperture Radar (SAR) is widely used in the earth observation. With the increase of resolution and swath width, the simulation data has a massive increase, which boosts the new requirements of efficiency. Through analyzing the redundancy in the raw data simulation based on Graphics Processing Unit (GPU), a fast simulation method considering reduction of redundant computation is realized by the multi-GPUs and Message Passing Interface (MPI). The results show that the efficiency of 4-GPUs increases 2 times through the redundant reduction, and the hardware cost decreases by 50%, thus the overall speedup achieves 350 times than the traditional CPU simulation.
- Circular scanning SAR,
- Raw data simulation,
- GPU parallel,
- Computing optimization

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References(20)

References

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