Development of a ship classification method based on Convolutional neural network and Cyclostationarity Analysis

Abstract

A few decades ago, the classification of ships by means of their acoustics signature depended on the trained ears of SONAR system operators. With the advances of machine and deep learning techniques, new methods of classification were proposed in order to assist the operator to identify and classify the acoustic event. In this scenario, this paper presents a passive SONAR classification method based on Cyclostationary Analysis and Convolutional Neural Networks. The Spectral Coherence of the ship-radiated noise was extract, and served as input for a modified MobileNetV2 Convolutional Neural Network classifier. The method was benchmarked in two different dataset and was able to discriminate different classes. In parallel, another three classifiers based on Support Vector Machine were developed. Comparison with previous articles that used the same database was made, as well as, an evaluation of noise immunity was conducted. It was observed that the proposed method presented higher accuracy, even for low signal-to-noise ratios.