GPU Acceleration for Synthetic Aperture Sonar Image Reconstruction

Isaac D. Gerg, Daniel C. Brown, Stephen G. Wagner, Daniel Cook, Brian N. O'Donnell, Thomas Benson, Thomas C. Montgomery

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations


Synthetic aperture sonar (SAS) image reconstruction, or beamforming as it is often referred to within the SAS community, comprises a class of computationally intensive algorithms for creating coherent high-resolution imagery from successive spatially varying sonar pings. Image reconstruction is usually performed topside because of the large compute burden necessitated by the procedure. Historically, image reconstruction required significant assumptions in order to produce real-time imagery within an unmanned underwater vehicle's (UUV's) size, weight, and power (SWaP) constraints. However, these assumptions result in reduced image quality. In this work, we describe ASASIN, the Advanced Synthetic Aperture Sonar Imagining eNgine. ASASIN is a time domain backprojection image reconstruction suite utilizing graphics processing units (GPUs) allowing real-time operation on UUVs without sacrificing image quality. We describe several speedups employed in ASASIN allowing us to achieve this objective. Furthermore, ASASIN's signal processing chain is capable of producing 2D and 3D SAS imagery as we will demonstrate. Finally, we measure ASASIN's performance on a variety of GPUs and create a model capable of predicting performance. We demonstrate our model's usefulness in predicting run-time performance on desktop and embedded GPU hardware.

Original languageEnglish (US)
Title of host publication2020 Global Oceans 2020
Subtitle of host publicationSingapore - U.S. Gulf Coast
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728154466
StatePublished - Oct 5 2020
Event2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020 - Biloxi, United States
Duration: Oct 5 2020Oct 30 2020

Publication series

Name2020 Global Oceans 2020: Singapore - U.S. Gulf Coast


Conference2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Oceanography
  • Automotive Engineering
  • Instrumentation
  • Signal Processing


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