论文标题
自适应贝叶斯波束成像成像，通过边缘化声音速度
Adaptive Bayesian Beamforming for Imaging by Marginalizing the Speed of Sound
论文作者
论文摘要
基于数组信号处理的成像方法通常需要基于声学信号的方法的介质的固定传播速度或声速（SOS）。使用这些方法形成的图像的分辨率受到假定的SO的强烈影响，由于多径，非线性传播和不均匀的介质，因此最多可以选择。在这封信中，我们提出了一种贝叶斯的方法，以将SOS对光束形成器的影响边缘化。我们将贝叶斯的排序估计调整为成像设置，并在SOS的后部整合了流行的最小方差波束形式。为了有效地解决贝叶斯积分，我们使用数值高斯正交。我们将波束形成方法应用于多径和非线性传播丰富的浅水声纳成像。我们将最小差异响应（MVDR）束缚器与最小方差进行比较，并证明其贝叶斯对应物可实现改善的范围和方位角分辨率，同时有效地抑制了多径伪影。
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.
