An Optimized GPU Implementation of the MVDR Beamformer for Active Sonar Imaging

Abstract

The minimum variance distortionless response (MVDR) beamformer has recently been proposed as an attractive alternative to conventional beamformers in active sonar imaging. Unfortunately, it is very computationally complex because a spatial covariance matrix must be estimated and inverted for each image pixel. This may discourage its unnecessary use in sonar systems which are continuously being pushed to ever higher imaging ranges and resolutions. In this study, we show that for active sonar systems up to 32 channels, the computation time can be significantly reduced by performing arithmetic optimizations, and by implementing the MVDR beamformer on a graphics processing unit (GPU). We point out important hardware limitations for these devices, and assess the design in terms of how efficiently it is able to use the GPU's resources. On a quad-core Intel Xeon system with a high-end Nvidia GPU, our GPU implementation renders more than a million pixels per second (1 MP/s). Compared to our initial central processing unit (CPU) implementation, the optimizations described herein led to a speedup of more than two orders of magnitude, or an expected five to ten times improvement had the CPU received similar optimization effort. This throughput enables real-time processing of sonar data, and makes the MVDR a viable alternative to conventional methods in practical systems.