用独立成份分析算法实现水声信号盲分离
Blind separation of underwater acoustic signals via independent component analysis algorithms
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摘要: 独立成份分析算法是在研究信号盲分离过程中出现的一种新方法,本文试图将几种独立成份分析算法用于分离水声信号。分析和比较了5种算法的性能,并用仿真信号对算法进行了仿真,阐述了独立成份分析算法分离水声信号的不足。针对含噪声模型,提出了一种基于独立成份分析算法成功分离水声信号的方法。Abstract: Independent component analysis (ICA) algorithms embody an emerging method for blind separation of signals. Present paper applies several ICA algorithms to separate underwater acoustic signals. Simulating with artifical signals, five ICA algorithms are analyzed and compared. The shortage of these algorithms to be used to separate underwater acoustic signals is also pointed out. Based on a noisy ICA model, a method is proposed which separates underwater acoustic signals in presence of noise successfully.