Mismatch-Robust Underwater Acoustic Localization Using A Differentiable Modular Forward Model

• URL: http://arxiv.org/abs/2503.23260v1
• Date: Sun, 30 Mar 2025 00:12:20 GMT
• Title: Mismatch-Robust Underwater Acoustic Localization Using A Differentiable Modular Forward Model
• Authors: Dariush Kari, Yongjie Zhuang, Andrew C. Singer,
• Abstract summary: We exploit a pre-trained neural network for the acoustic wave propagation in a gradient-based framework to estimate the source location.<n>We introduce a physics-inspired modularity in the forward model that enables us to learn the path lengths of the multipath structure in an end-to-end training manner.
• Score: 4.2671394819888455
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we study the underwater acoustic localization in the presence of environmental mismatch. Especially, we exploit a pre-trained neural network for the acoustic wave propagation in a gradient-based optimization framework to estimate the source location. To alleviate the effect of mismatch between the training data and the test data, we simultaneously optimize over the network weights at the inference time, and provide conditions under which this method is effective. Moreover, we introduce a physics-inspired modularity in the forward model that enables us to learn the path lengths of the multipath structure in an end-to-end training manner without access to the specific path labels. We investigate the validity of the assumptions in a simple yet illustrative environment model.

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