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.

翻译：水下声学环境估计是遥感应用中的一项重要且具有挑战性的任务。现有估计方法需依赖较高的信号强度并需解决脆弱的回波标记问题方能有效工作。在先前发表的研究中，我们提出了一种基于深度学习的通用二维环境估计方法，该方法在仿真和实际实验场景中均超越了现有技术水平。该方法的局限在于需要用户预先提供反射边界的数量与位置信息，且其神经网络需针对不同环境重新训练。通过采用更先进的神经网络和时延估计技术，本文提出的改进方法不再需要边界数量或位置的先验知识，能够准确估计含有一个或两个边界的二维环境。未来工作将把所提方法扩展至更多边界及更大规模的环境。