期刊文献+

ADS与Matlab协同仿真下的盲源分离实现 被引量:1

Implementation of Blind Source Separation Based on Co-simulation of ADS with Matlab
在线阅读 下载PDF
导出
摘要 盲源分离是信号处理领域发展起来的一个新分支,越来越受到人们的关注和重视,并广泛应用于无线通信、生物医学、语音和图像处理等诸多领域。其优势在于能在各源信号和传输信道参数均未知的情况下,仅仅利用源信号的多个混合信号就能恢复源信号的各个独立成分。ADS作为一款强大的仿真设计软件,在数字信号处理领域已得到了广泛的运用。通过在ADS中搭建仿真平台,并利用ADS与Matlab协同仿真,在ADS中实现盲源分离,可以很好地发挥两种软件各自的优点,从而为从事系统和电路研究人员提供一种新的仿真方法。 Blind Source Separation (BSS) is one of the new branches in signal processing field and has been paid more and more attention, and it is applied in the wireless communication, biomedical, speech and image processing and so on. The specific advantage of BSS is that it can recover every independent component of source signal just using the multi - mixed signal of source signal, without knowing every source signal and the coefficients of transmission channel. ADS has been applied widely as the powerful simulation software in digital signal processing field. By building the simulation platform in the ADS and using the co - simulation of ADS with Matlab,the blind source separation can be realized in the ADS and it can make full use of advantage of the both softwares, so it provides a new method for the people who are researching in system and electrocircuit.
作者 杜元军 高勇
出处 《现代电子技术》 2009年第13期73-75,78,共4页 Modern Electronics Technique
关键词 ADS 协同仿真 盲源分离 MATLAB ADS co - simulation BSS Matlab
  • 相关文献

参考文献6

二级参考文献63

  • 1张贤达,保铮.盲信号分离[J].电子学报,2001,29(z1):1766-1771. 被引量:212
  • 2[1]Amari S.A theory of adaptive pattern classifiers [J].IEEE Trans.Electronic Computers,1967,16:299-307.
  • 3[2]Amari S.Natural gradient works efficiently in learning [J].Neural Comoutation,1998,10:251-276.
  • 4[3]Amari S,Cichocki A.Adaptive blind signal processing:Neural network approaches [J].Proc.IEEE,1998 ,86:2026-2048.
  • 5[4]Basak J,Amari S.Blind separation of uniformly distributed signals:A general approach [J].IEEE Trans.Neural Networks,1999,10:l173-1185.
  • 6[5]Bell A J,Sejnowski T J.An information-maximization approach to blind separation and blind deconvolution [J].Neural Computation,1995,7:1129-1159.
  • 7[6]Burel G.Blind separation of .sources:A nonlinear neural algorithm [J].Neural Networks,1992,5:937-947.
  • 8[7]Cao X R,Liu R W.A general approach to blind source separation [J].IEEE Trans.Signal Processing,1996,44:562-571.
  • 9[8]Cardoso J F.Blind signal separation:Statistical principles [J].Proc.IEEE,1998,86(10):2009-2025.
  • 10[9]Cardoso J F,Laheld B.Equivariant adaptive source separation [J].IEEE Trans.Signal Processing,1996,44:3017 - 3029.

共引文献216

同被引文献6

引证文献1

二级引证文献6

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部