Blind Identification of Underdetermined Mixtures with Complex Sources Using the Generalized Generating Function

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• 作者：Fanglin Gu (1) (2)
  Hang Zhang (2)
  Shan Wang (1)
  Desheng Zhu (2)
  
  1. College of Electronics Science and Engineering ; National University of Defense Technology ; Changsha聽 ; 410073 ; People鈥檚 Republic of China
  2. Institute of Communication Engineering ; PLA University of Science and Technology ; Nanjing聽 ; 210007 ; People鈥檚 Republic of China
  
• 关键词：Blind identification ; Generalized generating function ; Tensor decomposition ; Underdetermined mixtures
• 刊名：Circuits, Systems, and Signal Processing
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：34
• 期：2
• 页码：681-693
• 全文大小：390 KB
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• 刊物类别：Engineering
• 刊物主题：Electronic and Computer Engineering
  
• 出版者：Birkh盲user Boston
• ISSN：1531-5878

文摘

The generalized generating function (GGF), which can exploit the statistical information carried on complex-valued signals efficiently by treating its real and imaginary parts as a whole and offer an elegant algebraic structure, has been proposed by authors for blind identification of mixtures. In this paper, we extend the GGF-based method to be able to deal with the challenging underdetermined mixtures with complex-valued sources. A new algorithm named ALSGGF, in which the mixing matrix is estimated by decomposing the tensor constructed from the higher conjugate derivative of the second GGF of the observations with alternating least squares algorithm, is proposed. Furthermore, we show that the conjugate derivatives of different orders of the second GGF can be used jointly in a simple way to improve the performance. Simulation experiments validate the superiority of the proposed ALSGGF algorithms.