2022/12/07 by Kyurae Kim, Kim, Kyurae, Simon Maskell +3
Computer Science · Earth and Planetary Sciences · Engineering · #FOS: Electrical engineering #Flow Measurement and Analysis #Signal Processing (eess.SP) #Speech and Audio Processing #Underwater Acoustics Research #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2212.03824
openalex publication_date 2022/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.