Análise de Amostras Sintéticas de Sinais de Sonar Passivo Geradas por Redes Neurais Generativas Adversariais

Abstract

In naval warfare, several techniques have been developed for the detection and classification of war vessels. Given the confidential nature of the data it is extremely difficult to get a hold of large quantities of data which makes it extremely hard to use techniques that rely on abundant data, such as deep learning. This paper proposes the use of generative adversarial neural networks for the generation of synthetic samples that can later be used in training of classifiers. This paper focuses on the generation process and the qualifying of such samples.