Park Yongsung, Gerstoft Peter, Seong Woojae
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093-0238, USA.
Department of Naval Architecture and Ocean Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
J Acoust Soc Am. 2019 Mar;145(3):1427. doi: 10.1121/1.5094345.
A grid-free compressive sensing (CS) based method for extracting the normal modes of acoustic propagation in the ocean waveguide from vertical line array (VLA) data is presented. Extracting the normal modes involves the estimation of mode horizontal wavenumbers and the corresponding mode shapes. Sparse representation of the waveguide propagation using modes at discrete horizontal wavenumbers enables CS to be applied. Grid-free CS, based on group total-variation norm minimization, is adopted to mitigate the issues of the wavenumber search grid discretization in the conventional CS. In addition, the suggested method can process multiple sensor data jointly, which improves performance in estimation over single sensor data processing. The method here uses data on a VLA from a source at several ranges, and processes the multiple sensor data at different depths jointly. The grid-free CS extracts the mode wavenumbers and shapes even with no a priori environmental knowledge, a partial water column spanning array data, and without the mode orthogonality condition. The approach is illustrated by numerical simulations and experimental SWellEx-96 (shallow water evaluation cell experiment 1996) data.
提出了一种基于无网格压缩感知（CS）的方法，用于从垂直线列阵（VLA）数据中提取海洋波导中的声传播简正模式。提取简正模式涉及模式水平波数和相应模式形状的估计。利用离散水平波数处的模式对波导传播进行稀疏表示，使得能够应用CS。采用基于组总变差范数最小化的无网格CS，以减轻传统CS中波数搜索网格离散化的问题。此外，所提出的方法可以联合处理多个传感器数据，这比单传感器数据处理在估计性能上有所提高。这里的方法使用来自多个距离处源的VLA上的数据，并联合处理不同深度的多个传感器数据。即使没有先验环境知识、部分水柱跨度阵列数据且没有模式正交性条件，无网格CS也能提取模式波数和形状。通过数值模拟和实验SWellEx - 96（1996年浅水环境评估单元实验）数据对该方法进行了说明。
