Adapting the minimum variance beamformer to a graphics processing unit for active sonar imaging systems

Abstract

The MVDR beamformer has been shown to improve active sonar image quality compared to conventional methods. Unfortunately, it is also significantly more computationally expensive because a spatial covariance matrix must be estimated and inverted for each image pixel. We target this challenge by altering and mapping the MVDR beamformer to a GPU, and suggest three different solutions depending on the system size. For systems with relatively few channels, we suggest arithmetic optimizations for the estimation step, and show how a GPU can be used to yield image creation rates of more than 1 Mpx/s. For larger systems we show that frequency domain processing is preferable, as this promotes high processing rates at a negligible reduction in image quality. These GPU implementations consistently reduced the runtime by 2–3 orders of magnitude compared to our reference C and Matlab implementations. For even larger systems we suggest employing the LCA beamformer. It does not calculate a weightset, but merely computes the beamformer output for each of a predefined set of weights, and selects the one that best fulfils the MVDR criterion. The LCA creates images with a quality comparable to MVDR, and it is perfectly suited for a GPU.