Simulation-based training of neural networks for low-frequency sonar target classification

Abstract

Artificial Intelligence (AI) is widely used in various fields, but obtaining sufficient underwater acoustic data for AI training remains challenging. Even if simulation data are used for training, the classification accuracy could be lowered due to changes in the environment around the target. This study presents the research findings on training neural networks for target classification using data generated through simulations. We trained neural networks using target scattering signals obtained through finite element analysis for various environments. Subsequently, we utilized trained neural networks to classify the spherical shells on the seabed acquired in water tank experiments. To solve the problem of lack of data for AI training, we generated training data by applying realistic variations of parameters such as sound speed of water, target material properties, and thickness. Such training data provide more meaningful information than noise-added data. The targets considered were spherical shells with and without internal spaces. Although the target classification accuracy is not yet high enough, it is expected to be used to overcome the lack of data by applying to untrained targets. [Work supported by KIMST funded by the Agency of Korea Coast Guard (KIMST-20210547) and by KRISO funded by Ministry of Oceans and Fisheries (PES4380).]