Adaptive Bayesian beamforming for imaging by marginalizing the speed of sound

Imaging methods based on array signal processing and acoustic signals often require a fixed speed of sound (SoS). The resolution of the resulting images is strongly affected by the assumed SoS, which is challenging to select. This letter proposes a beamformer for imaging that improves resolution by marginalizing the SoS using a Bayesian probabilistic model. We compare the proposed beamformers with the standard minimum-variance distortionless response beamformer and demonstrate that their Bayesian counterparts achieve improved range and azimuthal resolution. Furthermore, we demonstrate that the marginal likelihood of the model can be used for imaging and that it exhibits good multipath artifact suppression and improved resolution.