Linear State Space Feedforward and Feedback Structures for Blind Source Recovery in Dynamic Environments

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• 作者：Khurram Waheed and Fathi M. Salem
• 关键词：adaptive signal processing ; blind source recovery ; feedback network ; feedforward network ; Kullback ; Lieblar divergence ; multi ; channel blind source deconvolution ; natural gradient ; state space ; unsupervised learning
• 刊名：Neural Processing Letters
• 出版年：2005
• 出版时间：December 2005
• 年：2005
• 卷：22
• 期：3
• 页码：325-344
• 全文大小：350 KB
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Complexity
  Artificial Intelligence and Robotics
  Electronic and Computer Engineering
  Operation Research and Decision Theory
  
• 出版者：Springer Netherlands
• ISSN：1573-773X

文摘

Blind Source Recovery (BSR) denotes recovery of original sources or signals from environments, which may include convolution, temporal variation, and even non-linearity—without necessarily performing explicit environment identification. This paper presents two separate multiple-input multiple-output (MIMO) structures for dynamic linear Blind Source Recovery (BSR) of multiple stochastically independent source signals. We propose linear state space models for both the mixing environment and the demixing (or the recovering) adaptive network. The demixing network may assume either the feedforward or the feedback state space configuration. Separate algorithms have been derived for the adaptive estimation of the feedforward and the feedback demixing/recovering network’s parameters. These algorithms are based on the multivariable optimization theory and utilize the Riemannian contra-variant gradient (or the natural gradient) search under the constraints of a state space representation. Auxiliary conditions for the convergence of these algorithms have also been derived and discussed. Illustrative simulation examples have been included to demonstrate the successful adaptation results for both proposed demixing network structures in the case of an infinite impulse response (IIR)-type mixing environment for example source statistical distributions.