论文标题
通过卷积神经网络在水下声学中的直接定位：一种数据驱动的方法
Direct Localization in Underwater Acoustics via Convolutional Neural Networks: A Data-Driven Approach
论文作者
论文摘要
直接定位（DLOC）方法，它使用观察到的数据将源定位在一个步骤过程中的未知位置，通常优于其间接的两步对应物（例如，使用Arrivals的时间差异）。但是，水下声学DLOC方法需要对环境的先验知识，并且计算昂贵，因此很慢。我们提出，据我们所知，这是第一个数据驱动的DLOC方法。受经典和现代最佳模型的DLOC解决方案的启发，并利用了卷积神经网络（CNN）的功能，我们设计了一个基于CNN的整体解决方案。我们的方法包括一个专门的输入结构，体系结构，损失功能和渐进式培训程序，在更广泛的机器学习背景下具有独立的兴趣。我们证明我们的方法优于有吸引力的替代方案，并且渐近地与基于Oracle的最佳模型解决方案的性能匹配。
Direct localization (DLOC) methods, which use the observed data to localize a source at an unknown position in a one-step procedure, generally outperform their indirect two-step counterparts (e.g., using time-difference of arrivals). However, underwater acoustic DLOC methods require prior knowledge of the environment, and are computationally costly, hence slow. We propose, what is to the best of our knowledge, the first data-driven DLOC method. Inspired by classical and contemporary optimal model-based DLOC solutions, and leveraging the capabilities of convolutional neural networks (CNNs), we devise a holistic CNN-based solution. Our method includes a specifically-tailored input structure, architecture, loss function, and a progressive training procedure, which are of independent interest in the broader context of machine learning. We demonstrate that our method outperforms attractive alternatives, and asymptotically matches the performance of an oracle optimal model-based solution.
