4 papers
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…
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…
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…