Blind Source Separation of Underwater Acoustic Signal by Use of Negentropy-Based Fast ICA Algorithm
Tu Shijie, Hang Chen
Abstract
Tu Shijie, Hang Chen
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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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