2870

Using Graphics Processors to Accelerate Synthetic Aperture Sonar Imaging via Backpropagation

Daniel P. Campbell, Daniel A. Cook
Georgia Tech Research Institute / Sensors and Electromagnetic Applications Laboratory
Symposium on Application Accelerators in High Performance Computing, 2010

@article{campbell2010using,

   title={Using Graphics Processors to Accelerate Synthetic Aperture Sonar Imaging via Backpropagation},

   author={Campbell, D.P. and Cook, D.A.},

   booktitle={Application Accelerators in High Performance Computing, 2010 Symposium, Papers},

   year={2010}

}

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This paper describes the use of graphics processors to accelerate the backpropagation method of forming images in Synthetic Aperture Sonar (SAS) systems. SAS systems coherently process multiple pulses to provide a higher level of detail in the resolved image than is otherwise possible with a single pulse. Several models are available to resolve an image from the pulse return data; the backpropagation model is the most accurate and flexible, but also the most computationally intensive. Less flexible and accurate algorithms are frequently used because the time to resolve an image via backpropagation is held to be intolerable. A GPU-based implementation of backpropagation was developed at GTRI and inserted into sonar and radar algorithm research testbed systems. The GPU accelerated implementation formed a 4000 x 4400 SAS image from 60 seconds of sonar data in 7 seconds using 8 GPUs. This was 275x faster than a C-based implementation executing on an 8-core i7 platform, and provides an otherwise timeprohibitive technique to sonar and algorithm researchers for use in prototyping environments.
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