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基于獨立矢量基的波達方向估計

李小軍,  張賢達,  保錚

李小軍, 張賢達, 保錚. 基于獨立矢量基的波達方向估計[J]. 電子與信息學(xué)報, 2002, 24(10): 1297-1303.
引用本文: 李小軍, 張賢達, 保錚. 基于獨立矢量基的波達方向估計[J]. 電子與信息學(xué)報, 2002, 24(10): 1297-1303.
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.

基于獨立矢量基的波達方向估計

Estimation direction of angle based on independent vector basis

  • 摘要: 獨立分量分析可從線性混合的信號中分離出彼此獨立的信號源,也就是利用獨立矢量基對混合的信號進行分離,這種方法在一定條件下與盲信源分離等價。該文利用通過非線性最小均方準則獲得的獨立矢量基,并且根據(jù)其所具有的特性,將其應(yīng)用于均勻線陣的信號波達方向估計。
    Abstract: 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]張賢達,現(xiàn)代信號處理,北京,清華大學(xué)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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出版歷程
  • 收稿日期:  2000-09-18
  • 修回日期:  2001-04-20
  • 刊出日期:  2002-10-19

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