4 papers
Joint Source-Environment Adaptation of Data-Driven Underwater Acoustic Source Ranging Based on Model Uncertainty
Dariush Kari, Hari Vishnu, Andrew C. Singer
Adapting pre-trained deep learning models to new and unknown environments remains a major challenge in underwater acoustic localization. We show that although the performance of pr…
Joint Source-Environment Adaptation for Deep Learning-Based Underwater Acoustic Source Ranging
Dariush Kari, Andrew C. Singer
In this paper, we propose a method to adapt a pre-trained deep-learning-based model for underwater acoustic localization to a new environment. We use unsupervised domain adaptation…
Mismatch-Robust Underwater Acoustic Localization Using A Differentiable Modular Forward Model
Dariush Kari, Yongjie Zhuang, Andrew C. Singer
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 pr…
Estimating the Number and Locations of Boundaries in Reverberant Environments with Deep Learning
Toros Arikan, Luca M. Chackalackal, Fatima Ahsan +4
Underwater acoustic environment estimation is a challenging but important task for remote sensing scenarios. Current estimation methods require high signal strength and a solution…