GPU Acceleration for Synthetic Aperture Sonar Image Reconstruction

TL;DR

This paper introduces ASASIN, a GPU-accelerated software suite for real-time high-quality synthetic aperture sonar image reconstruction, capable of producing 2D and 3D imagery on UUVs.

Contribution

The paper presents ASASIN, a novel GPU-based backprojection algorithm enabling real-time SAS image reconstruction without quality compromises.

Findings

Achieves real-time processing on UUVs

Capable of producing 2D and 3D SAS images

Provides a performance prediction model for GPUs

Abstract

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.