School of Electrical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30308, USA.
J Acoust Soc Am. 2012 Jul;132(1):90-102. doi: 10.1121/1.4728224.
Source localization by matched-field processing (MFP) generally involves solving a number of computationally intensive partial differential equations. This paper introduces a technique that mitigates this computational workload by "compressing" these computations. Drawing on key concepts from the recently developed field of compressed sensing, it shows how a low-dimensional proxy for the Green's function can be constructed by backpropagating a small set of random receiver vectors. Then the source can be located by performing a number of "short" correlations between this proxy and the projection of the recorded acoustic data in the compressed space. Numerical experiments in a Pekeris ocean waveguide are presented that demonstrate that this compressed version of MFP is as effective as traditional MFP even when the compression is significant. The results are particularly promising in the broadband regime where using as few as two random backpropagations per frequency performs almost as well as the traditional broadband MFP but with the added benefit of generic applicability. That is, the computationally intensive backpropagations may be computed offline independently from the received signals, and may be reused to locate any source within the search grid area.
声源定位的匹配场处理（MFP）通常涉及求解大量计算密集型偏微分方程。本文介绍了一种通过“压缩”这些计算来减轻计算工作量的技术。该技术借鉴了最近发展起来的压缩感知领域的关键概念，展示了如何通过反向传播一小部分随机接收向量来构建格林函数的低维代理。然后，通过在压缩空间中对这个代理和记录的声数据的投影进行多次“短”相关运算，就可以定位声源。在 Pekeris 海洋波导中的数值实验表明，即使在压缩显著的情况下，这种压缩版本的 MFP 与传统 MFP 一样有效。在宽带情况下，结果尤其有希望，因为每频率使用两个随机反向传播几乎与传统的宽带 MFP 一样有效，但具有通用适用性的额外好处。也就是说，计算密集型的反向传播可以在与接收信号无关的情况下离线计算，并可重复用于定位搜索网格区域内的任何声源。
