  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.
