用可微分模块化模型提升声学定位在环境不匹配下的鲁棒性
Mismatch-Robust Underwater Acoustic Localization Using A Differentiable Modular Forward Model
- 用神经网络建模声波传播，结合梯度优化定位声源
- 推理时同步优化网络权重，缓解训练测试数据不一致问题
- 模块化设计实现端到端多路径长度学习，无需路径标签
本文研究环境不匹配下的水下声学定位问题。通过将预训练的神经网络嵌入基于梯度的优化框架，实现声源位置估计。为缓解训练数据与测试数据间的差异影响，提出在推理阶段同时优化网络权重，并给出该方法有效的理论条件。此外，引入物理启发的模块化前向模型，可在无需具体路径标签的情况下，以端到端方式学习多路径结构的路径长度。我们在一个简单但具有代表性的环境模型中验证了假设的有效性。
原文摘要 · Abstract (English)
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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