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Volume 24 Issue 10
Oct.  2002
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Li Xiaojun, Zhang Xianda, Bao Zheng . Estimation direction of angle based on independent vector basis[J]. Journal of Electronics & Information Technology, 2002, 24(10): 1297-1303.
Citation: Li Xiaojun, Zhang Xianda, Bao Zheng . Estimation direction of angle based on independent vector basis[J]. Journal of Electronics & Information Technology, 2002, 24(10): 1297-1303.

Estimation direction of angle based on independent vector basis

  • Received Date: 2000-09-18
  • Rev Recd Date: 2001-04-20
  • Publish Date: 2002-10-19
  • Independent component analysis aims to find a linearly transformation to coordinates in which the data are maximally statistical independent. In the method, by using the independent vector basis, the mixed signal will be decomposed to the independent components. Under some conditions, it is equivalent to the blind source separation. In this paper, the independent vector basis can be got by using the nonlinear minimizing mean square criterion. It can estimate the DOA of ULA by using the character of the independent vector basis.
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  • S. Amari.[J].A. Cichocki, H. H. Yang, A new learning algorithm for blind source separation, In G.Tesauro, M. C. Mozer, M. E. Hasselmo (Eds.), Advances in Neural Information Processing,Cambridge, MA: MIT Press.1996,:-[2]A. Bell, T. Sejnowski, An information maximization approach to blind separation and blind deconvolution, Neural Computation, 1995, 7(6), 1129-1159.[3]P. Comon, Independent component analysis, A new concept? Signal Processing, 1994, 36(3),287-314.[4]R. Everson, S. Roberts, Independent component analysis: a flexible nonlinearity and decorrelating manifold approach, Neural Computation, 1999, 11(7), 1957-1983.[5]C. Jutten, J. Herault, Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture, Signal Processing, 1991, 24(1), 1 10.[6]J. karhunen, J. Joutsensalo, Representation and separation of signals using nonlinear PCA type learning, Neural Networks, 1994, 7(1), 113-127.[7]D. Mackay, Maximum likelihood and covariant algorithms for independent component analysis,Tech. Rep., Cambridge, 1996. [8]S. Amari, Natural gradient works efficiently in learning, Neural Computation, 1998, 10(1), 251 276.[8]J. Karhunen, P. Pajunen, E. Oja, The nonlinear PCA criterion in blind source separation: Relations with other approaches, Neurocomputing, 1998, 22(1), 5-20.[9]B. Yang, Projection approximation subspace tracking, IEEE Trans. on Signal Processing, 1995,SP-43(1), 95-107.[10]张贤达,现代信号处理,北京,清华大学出版社,1995,第4章.[11]J. Karhunen.[J].P. Pajunen, Blind source separation using least-squares type adaptive algorithms,ICASSP97, Munich, Germany, April 21-2.1997,:-
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