Tichavský Petr, Koldovský Zbynek, Yeredor Arie, Gómez-Herrero Germán, Doron Eran
Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, 182 08 Prague8, Czech Republic.
IEEE Trans Neural Netw. 2008 Mar;19(3):421-30. doi: 10.1109/TNN.2007.908648.
Blind inversion of a linear and instantaneous mixture of source signals is a problem often encountered in many signal processing applications. Efficient fastICA (EFICA) offers an asymptotically optimal solution to this problem when all of the sources obey a generalized Gaussian distribution, at most one of them is Gaussian, and each is independent and identically distributed (i.i.d.) in time. Likewise, weights-adjusted second-order blind identification (WASOBI) is asymptotically optimal when all the sources are Gaussian and can be modeled as autoregressive (AR) processes with distinct spectra. Nevertheless, real-life mixtures are likely to contain both Gaussian AR and non-Gaussian i.i.d. sources, rendering WASOBI and EFICA severely suboptimal. In this paper, we propose a novel scheme for combining the strengths of EFICA and WASOBI in order to deal with such hybrid mixtures. Simulations show that our approach outperforms competing algorithms designed for separating similar mixtures.
源信号线性瞬时混合的盲反演是许多信号处理应用中经常遇到的问题。当所有源都服从广义高斯分布，其中至多有一个是高斯分布，且每个源在时间上独立同分布（i.i.d.）时，高效快速独立成分分析（EFICA）为该问题提供了一个渐近最优解。同样，当所有源都是高斯分布且可以建模为具有不同频谱的自回归（AR）过程时，加权调整二阶盲辨识（WASOBI）是渐近最优的。然而，实际生活中的混合信号可能同时包含高斯AR源和非高斯i.i.d.源，这使得WASOBI和EFICA严重次优。在本文中，我们提出了一种新颖的方案，结合EFICA和WASOBI的优势来处理这种混合混合信号。仿真表明，我们的方法优于为分离类似混合信号而设计的竞争算法。
