高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于独立矢量基的波达方向估计

李小军 张贤达 保铮

李小军, 张贤达, 保铮. 基于独立矢量基的波达方向估计[J]. 电子与信息学报, 2002, 24(10): 1297-1303.
引用本文: 李小军, 张贤达, 保铮. 基于独立矢量基的波达方向估计[J]. 电子与信息学报, 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.

基于独立矢量基的波达方向估计

Estimation direction of angle based on independent vector basis

  • 摘要: 独立分量分析可从线性混合的信号中分离出彼此独立的信号源,也就是利用独立矢量基对混合的信号进行分离,这种方法在一定条件下与盲信源分离等价。该文利用通过非线性最小均方准则获得的独立矢量基,并且根据其所具有的特性,将其应用于均匀线阵的信号波达方向估计。
  • 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,:-
  • 加载中
计量
  • 文章访问数:  1964
  • HTML全文浏览量:  94
  • PDF下载量:  453
  • 被引次数: 0
出版历程
  • 收稿日期:  2000-09-18
  • 修回日期:  2001-04-20
  • 刊出日期:  2002-10-19

目录

    /

    返回文章
    返回