用深度学习自动识别混响环境中的边界数量和位置，无需先验信息。
Estimating the Number and Locations of Boundaries in Reverberant Environments with Deep Learning
- 基于深度学习与时间延迟估计，无需用户输入边界信息
- 可准确估计含1至2个边界的二维水下环境
- 适合需要自动环境感知的水下探测场景
水下声学环境估计在远程传感任务中至关重要但极具挑战性。现有方法依赖强信号和脆弱的回波标注问题解决方案才能有效。此前我们提出一种通用的深度学习方法，在仿真与真实实验中均优于当前最优水平。该方法的局限在于需用户提供边界数量和位置信息，且针对不同环境需重新训练神经网络。本文提出的改进方法利用更先进的神经网络与时间延迟估计技术，不再依赖边界数量和位置的先验知识，能够仅通过一次训练即可估计含一个或两个边界的二维环境。未来工作将扩展至更多边界及更大尺度环境。
原文摘要 · Abstract (English)
Underwater acoustic environment estimation is a challenging but important task for remote sensing scenarios. Current estimation methods require high signal strength and a solution to the fragile echo labeling problem to be effective. In previous publications, we proposed a general deep learning-based method for two-dimensional environment estimation which outperformed the state-of-the-art, both in simulation and in real-life experimental settings. A limitation of this method was that some prior information had to be provided by the user on the number and locations of the reflective boundaries, and that its neural networks had to be re-trained accordingly for different environments. Utilizing more advanced neural network and time delay estimation techniques, the proposed improved method no longer requires prior knowledge the number of boundaries or their locations, and is able to estimate two-dimensional environments with one or two boundaries. Future work will extend the proposed method to more boundaries and larger-scale environments.
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