Título: Neural Passive Sonar Signal Classification Using Independent Component Analysis
Autores: Simas Filho, Eduardo F. de; Moura, Natanael Nunes de; Seixas, José Manoel de
Resumo: Sonar systems use the underwater sound propagation to detect, identify and locate targets (such as vessels or shoals of fishes). One of the most important tasks in passive sonar signal processing is target identification, which relies on sonar operators who listen to the acoustic signatures and assign to them a certain class of vessel. When there are acoustic signals from multiple targets arriving at adjacent directions, target identification becomes a harder task due to cross-channel interference. The purpose of this work is to develop a neural design support system for target identification, specially in multiple target applications. In order to improve the discrimination performance when cross-interference is present, independent component analysis (ICA) was used as a preprocessing step. It is shown that the proposed approach improved considerably the discrimination efficiency in an experimental two-target problem.
Palavras-chave: Passive Sonar System; Spectral Analysis; LOFAR Analysis; Independent Component Analysis
Código DOI: 10.21528/CBIC2011-17.2
Artigo em pdf: st_17.2.pdf
Arquivo BibTex: st_17.2.bib