Single-channel blind source separation of underwater acoustic signals using improved NMF and FastICA

Abstract

When automatic monitoring buoys receive mixed acoustic signals from multiple underwater acoustic targets, the statistical blind source separation (BSS) task is used to separate the signals and identify vessel features, which is overly complex and needs improvement, especially noting that noise cancellation and stealth technologies are advancing rapidly. To fill this gap in capability, an improved non-negative matrix factorization (NMF) based BSS algorithm is built on a FastICA machine learning backbone. With this tool, the spatial and spectral correlation of underwater acoustic signals is introduced into the NMF algorithm improved by to resolve the non-convex and feature correlation problems commonly encountered by contemporary NMF algorithms. Moreover, the improved modulation feature adaptability of the NMF increases the local expressivity and independence of the decomposed base matrix, which is proven to meet the requirements of FastICA and used to improve the BSS effect of the FastICA. Simulated and empirical results show that compared with state-of-the-art FastICA and NMF based BSS algorithms, our novel approach obtains better signal-to-noise reduction and separation accuracy while maintaining superior target signal recognition features.