• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
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Volume 26 Issue 6
Dec.  2013
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Article Contents
XIA Jianghua, GUO Jin, WANG Xiaomin. Source Number Estimation of Underdetermined Blind Mixing Signals Based on Spatial Time-Frequency Distribution and Parallel Factor Analysis[J]. Journal of Southwest Jiaotong University, 2013, 26(6): 1084-1089. doi: 10.3969/j.issn.0258-2724.2013.06.018
Citation: XIA Jianghua, GUO Jin, WANG Xiaomin. Source Number Estimation of Underdetermined Blind Mixing Signals Based on Spatial Time-Frequency Distribution and Parallel Factor Analysis[J]. Journal of Southwest Jiaotong University, 2013, 26(6): 1084-1089. doi: 10.3969/j.issn.0258-2724.2013.06.018

Source Number Estimation of Underdetermined Blind Mixing Signals Based on Spatial Time-Frequency Distribution and Parallel Factor Analysis

doi: 10.3969/j.issn.0258-2724.2013.06.018
  • Received Date: 03 May 2012
  • Publish Date: 25 Dec 2013
  • To estimate the number of sources of underdetermined blind mixing signals, a novel algorithm based on spatial time-frequency distribution (STFD) and parallel factor analysis (PARAFAC) was proposed. The time-frequency distribution matrices corresponding to the single auto-terms time-frequency (TF) points were stacked in a three-order tensor, and the core consistency diagnostic (CORCONDIA) was performed to estimate the number of sources. Then the uniqueness of the three order tensor low rank decomposition was analyzed. This algorithm does not need to assume that source signal must satisfy the sparse and independence conditions, and does not require that the signal meets Gaussian distribution. In simulation the recognition accuracy rate is increased by 18 dB when the signal-to-noise ratio is -5 dB, which demonstrates the proposed recognition algorithm is effective.

     

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