Adaptive Bayesian Beamforming for Imaging by Marginalizing the Speed of Sound

TL;DR

This paper introduces a Bayesian beamforming method that marginalizes the unknown speed of sound to improve imaging resolution and suppress artifacts in challenging environments like shallow water sonar.

Contribution

It develops a Bayesian approach to integrate over the speed of sound, enhancing traditional beamformers for better imaging in complex media.

Findings

Improved range and azimuthal resolution compared to standard MVDR.

Effective suppression of multipath artifacts.

Demonstrated success in shallow water sonar imaging.

Abstract

Imaging methods based on array signal processing often require a fixed propagation speed of the medium, or speed of sound (SoS) for methods based on acoustic signals. The resolution of the images formed using these methods is strongly affected by the assumed SoS, which, due to multipath, nonlinear propagation, and non-uniform mediums, is challenging at best to select. In this letter, we propose a Bayesian approach to marginalize the influence of the SoS on beamformers for imaging. We adapt Bayesian direction-of-arrival estimation to an imaging setting and integrate a popular minimum variance beamformer over the posterior of the SoS. To solve the Bayesian integral efficiently, we use numerical Gauss quadrature. We apply our beamforming approach to shallow water sonar imaging where multipath and nonlinear propagation is abundant. We compare against the minimum variance distortionless response (MVDR) beamformer and demonstrate that its Bayesian counterpart achieves improved range and azimuthal resolution while effectively suppressing multipath artifacts.