Robust blind source separation algorithms using cumulants
Robust blind source separation algorithms using cumulants
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DOI：
10.1016/s0925-2312(02)00525-8
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发表时间：
2002-12-01
期刊：
影响因子：
6
通讯作者：
Cichocki, A
中科院分区：
文献类型：
--
作者：
Cruces, S;Castedo, L;Cichocki, A
在本文中，我们提出了一种新的独立源信号的盲分离方法，该方法在避免了正交混合矩阵的强加的同时，对于混合中存在的加性高斯噪声具有鲁棒性。我们证明，对于具有一定非零累积量和预先指定的尺度的广泛类源分布，分离始终是基于累积量的代价函数的鞍点。我们提出了一种准牛顿方法来确定这个鞍点。这使我们能够获得一组基于高阶统计量的分离算法，即使在存在大高斯噪声的情况下也能产生无偏估计，并且具有局部各向同性收敛的有趣性质。另一类包含二阶统计量的算法失去了以前理想的收敛特性，但在没有噪声的情况下提供了更精确的估计。大量的计算机模拟证实了所得到的算法的鲁棒性和优异的性能。(C) 2002 Elsevier Science B.V.版权所有
In this paper we propose a new approach to blind separation of independent source signals that, while avoiding the imposition of an orthogonal mixing matrix, is robust with respect to the existence of additive Gaussian noise in the mixture. We demonstrate that, for the wide class of source distributions with certain non-null cumulants and a pre-specified scaling, separation is always a saddle point of a cumulant-based cost function. We propose a quasi-Newton approach for determining this saddle point. This enables us to obtain a family of separation algorithms which, based on higher order statistics, yields unbiased estimates even in the presence of large Gaussian noise and has the interesting property of local isotropic convergence. Another family of algorithms that incorporates second-order statistics loses the former desirable convergence properties but it provides more precise estimates in the absence of noise. Extensive computer simulations confirm robustness and the excellent performance of the resulting algorithms. (C) 2002 Elsevier Science B.V. All rights reserved.