Enhancing generalization performance of anomaly detection-based active sonar classifier

Abstract

It is critical to automatically discriminate between target and clutter in developing future unmanned underwater surveillance systems. However, due to similar physical experiences between target echoes and clutter, active sonar classification remains a challenging problem. Recently, a novel anomaly detection-based active sonar classifier, bi-sphere anomaly detection (BiSAD), was proposed, demonstrating improved generalization performance over conventional supervised learning-based approaches. In the present study, we provide a brief overview of active sonar datasets and the BiSAD. Then, we explore modifications to BiSAD, aiming at further improving its generalization capabilities. Through examination using in situ active data, we validate the efficacy of these modifications, showing superior generalization performance compared to conventional supervised learning-based classifiers and the original BiSAD.