2015Unpublished venueRequires access

Blind Source Separation of Underwater Acoustic Signal by Use of Negentropy-Based Fast ICA Algorithm

Tu Shijie, Hang Chen

Open publisher page 10 citations

Abstract

Based on in-depth study of independent component analysis (ICA) method and signal independence measure algorithm based on negentropy, the author first conducts pretreatment of centering and whitening the mixed data of underwater acoustic signal, and then applies the negentropy-based fast ICA algorithm to the blind source separation of underwater acoustic signal and performs simulation experiment. The simulation result indicates that the negentropy-based fast ICA algorithm can effectively solve the blind source separation problems in the signal, this also shows that the method has certain universality and has extensive application prospect in the signal processing field.

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What this paper is about

Based on in-depth study of independent component analysis (ICA) method and signal independence measure algorithm based on negentropy, the author first conducts pretreatment of centering and whitening the mixed data of underwater acoustic signal, and then applies the negentropy-based fast ICA algorithm to the blind source separation of underwater acoustic signal and performs simulation experiment. The simulation result indicates that the negentropy-based fast ICA algorithm can effectively solve the blind source separation problems in the signal, this also shows that the method has certain universality and has extensive application prospect in the signal processing field.

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OpenAlex reports 10 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Based on in-depth study of independent component analysis (ICA) method and signal independence measure algorithm based on negentropy, the author first conducts pretreatment of centering and whitening the mixed data of underwater acoustic signal, and then applies the negentropy-based fast ICA algorithm to the blind source separation of underwater acoustic signal and performs simulation experiment. The simulation result indicates that the negentropy-based fast ICA algorithm can effectively solve the blind source separation problems in the signal, this also shows that the method has certain universality and has extensive application prospect in the signal processing field.

Key concepts: Negentropy, Independent component analysis, FastICA, Blind signal separation, Computer science, SIGNAL (programming language), Algorithm, Signal processing

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