Equivariant adaptive source separation
Equivariant adaptive source separation
复制标题
DOI：
10.1109/78.553476
复制
发表时间：
1996-12-01
影响因子：
5.4
通讯作者：
Laheld, BH
中科院分区：
文献类型：
--
作者：
Cardoso, JF;Laheld, BH
源分离是指当仅观测到具有未知系数的混合信号时，恢复出一组独立信号。本文介绍了一类用于源分离的自适应算法，该算法实现了等变估计的自适应版本，此后被称为基于独立性的等变自适应分离（EASI）。EASI算法基于串行更新的思想：这种特定形式的矩阵更新系统地产生了对实混合信号和复混合信号都具有简单结构的算法。最重要的是，EASI算法的性能不依赖于混合矩阵。特别是，收敛速度、稳定条件和干扰抑制水平仅取决于源信号的（归一化）分布。通过渐近性能分析给出了这些量的闭式表达式。等变这一主题在全文中都得到了强调。源分离问题具有一种潜在的乘法结构：参数空间形成一个（矩阵）乘法群。我们探讨了这一事实对EASI算法的实现、性能和优化所产生的（有利）影响。
Source separation consists of recovering a set of independent signals when only mixtures with unknown coefficients are observed. This paper introduces a class of adaptive algorithms for source separation that implements an adaptive version of equivariant estimation and is henceforth called equivariant adaptive separation via independence (EASI), The EASI algorithms are based on the idea of serial updating: This specific form of matrix updates systematically yields algorithms with a simple structure for both real and complex mixtures. Most importantly, the performance of an EASI algorithm does not depend on the mixing matrix. In particular, convergence rates, stability conditions, and interference rejection levels depend only on the (normalized) distributions of the source signals. Closed-form expressions of these quantities are given via an asymptotic performance analysis,The theme of equivariance is stressed throughout the paper, The source separation problem has an underlying multiplicative structure: The parameter space forms a (matrix) multiplicative group, We explore the (favorable) consequences of this fact on implementation, performance, and optimization of EASI algorithms.