caa a22 4500 606206477 CHVBK 20210128101011.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00034-014-9858-6 doi (NATIONALLICENCE)springer-10.1007/s00034-014-9858-6 Blind Identification of Underdetermined Mixtures with Complex Sources Using the Generalized Generating Function [Elektronische Daten] [Fanglin Gu, Hang Zhang, Shan Wang, Desheng Zhu] 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. Springer Science+Business Media New York, 2014 Blind identification nationallicence Generalized generating function nationallicence Tensor decomposition nationallicence Underdetermined mixtures nationallicence Gu Fanglin College of Electronics Science and Engineering, National University of Defense Technology, 410073, Changsha, People's Republic of China aut Zhang Hang Institute of Communication Engineering, PLA University of Science and Technology, 210007, Nanjing, People's Republic of China aut Wang Shan College of Electronics Science and Engineering, National University of Defense Technology, 410073, Changsha, People's Republic of China aut Zhu Desheng Institute of Communication Engineering, PLA University of Science and Technology, 210007, Nanjing, People's Republic of China aut Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/2(2015-02-01), 681-693 0278-081X 34:2<681 2015 34 34 https://doi.org/10.1007/s00034-014-9858-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00034-014-9858-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Gu Fanglin College of Electronics Science and Engineering, National University of Defense Technology, 410073, Changsha, People's Republic of China aut NATIONALLICENCE 700 1- Zhang Hang Institute of Communication Engineering, PLA University of Science and Technology, 210007, Nanjing, People's Republic of China aut NATIONALLICENCE 700 1- Wang Shan College of Electronics Science and Engineering, National University of Defense Technology, 410073, Changsha, People's Republic of China aut NATIONALLICENCE 700 1- Zhu Desheng Institute of Communication Engineering, PLA University of Science and Technology, 210007, Nanjing, People's Republic of China aut NATIONALLICENCE 773 0- Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/2(2015-02-01), 681-693 0278-081X 34:2<681 2015 34 34