Simulation-aided machine learning: Applications in underwater acoustic perception and explainability

Abstract

Underwater acoustic applications of machine learning are challenging due to sparsity, lack of diversity, and often uncertain labeling of training data. This is due to low levels of consumer relevance. One approach to the problem is to collect and label more data. However, this is expensive and unlikely to fully address the issue. A complementary approach is to use simulation for generating synthetic data and augmenting the machine learning pipeline. This also enables explainability by encoding human knowledge of the underlying physics. We present two case studies in simulation-aided machine learning. The first is an application to underwater munition dumpsite characterization using multi-view sonar surveys. Self-supervised learning has been used to pre-train a generative deep neural network to predict alternate views using unlabeled data. Subsequently, training is completed using a small volume of labelled data. Simulation was necessary for successful pre-training to continuously fill viewing angle gaps caused by limited and rigid survey directions. The second application is autonomous tactical planning for uncrewed underwater vehicles. Reinforcement learning policies have been trained to passively detect and localize underwater acoustic sources. These have been trained and evaluated in a fully synthetic environment, using a sophisticated propagation model together with empirical environmental parameters.