Underwater moving target detection using online robust principal component analysis and multimodal anomaly detection.

Abstract

In shallow water, reverberation complicates the detection of low-intensity, variable-echo moving targets, such as divers. Traditional methods often fail to distinguish these targets from reverberation, and data-driven methods are constrained by the limited data on intruding targets. This paper introduces the online robust principal component analysis and multimodal anomaly detection (ORMAD) method to address these challenges. ORMAD efficiently performs online low-rank and sparse decomposition while utilizing unsupervised multimodal anomaly detection to enhance detection performance. The multimodal anomaly detection process involves two phases: modality extraction and anomaly detection. During modality extraction, echo data are separated into echo structure and spatial trajectory modalities, providing complementary information that improves the network representation of both reverberation and moving targets. The subsequent anomaly detection phase unsupervisedly learns the modalities of fluctuating reverberation, thereby achieving stable reconstruction while maintaining sensitivity to moving targets. This sensitivity allows effective identification of moving targets by detecting reconstruction loss. Experimental results demonstrate that ORMAD effectively improves detection performance in complex reverberation scenarios. In a real-world sonar dataset, ORMAD increased the average precision for detecting diver targets from 60% to 75% compared to the state-of-the-art method.