Machine Learning Based Methods for Ship Radiated Noise Spectrogram Separation and Lloyd’s Mirror Pattern Extraction

This study presents an integrated machine learning framework for separating narrowband components and extracting Lloyd's mirror interference patterns from ship-radiated noise(SRN) spectrograms. The proposed methodology employs Independent Vector Analysis to separate narrowband spectral components from multi-hydrophone acoustic signals, subsequently applying DBSCAN and RANSAC algorithms for robust identification of parabolic Lloyd's mirror patterns in residual spectrograms. Experimental validation utilizing SRN data acquired during the SAVEX-15 sea trials demonstrates effective narrowband component separation, as verified through DEMON and LOFAR analyses, alongside accurate pattern extraction capabilities. The unsupervised framework exhibits enhanced reliability under adverse noise conditions and enables precise closest point of approach(CPA) estimation. The developed methodology offers an automated and robust solution for SRN analysis, significantly improving acoustic signal interpretation and target identification capabilities in maritime defense applications.