首页 | 本学科首页   官方微博 | 高级检索  
     

基于Hough变换的时频盲源分离算法
引用本文:郭靖,曾孝平.基于Hough变换的时频盲源分离算法[J].计算机工程与应用,2012,48(21):26-30.
作者姓名:郭靖  曾孝平
作者单位:1. 西南大学电子信息工程学院,重庆400715 ;重庆大学通信工程学院,重庆400044
2. 重庆大学通信工程学院,重庆,400044
基金项目:中央高校基本科研业务费专项资金资助(No.XDJK2009C035)
摘    要:1998年,Belouchrani,A和Amin,M.G基于时频分布提出了一种经典的时频盲源算法,不足是当有噪声存在时,性能会下降。主要考虑源噪声的盲源分离问题,以Wigner分布计算观测信号的时频阵并将其看做图像,利用Hough变换将信号检测转换为在参数空间寻找局部极大值的问题,运用自项点理论选择合适的矩阵进行联合近似对角化估计源信号。该方法扩展了盲源分离的限制条件,且通过把噪声能量扩展到整个参数平面而只选择信号能量占主导的时频点,对噪声具有一定的抑制能力。

关 键 词:源噪声  Wigner分布  Hough变换  盲源分离

Time-frequency blind source separation based on Hough transform
GUO Jing , ZENG Xiaoping.Time-frequency blind source separation based on Hough transform[J].Computer Engineering and Applications,2012,48(21):26-30.
Authors:GUO Jing  ZENG Xiaoping
Affiliation:1.School of Electronic and Information Engineering,Southwest University,Chongqing 400715,China 2.College of Communication Engineering,Chongqing University,Chongqing 400044,China
Abstract:A classical Blind Source Separation(BSS)algorithm based on Time-Frequency Distribution(TFD)was proposed by Belouchrani,A and Amin,M.G in 1998.When noise is present,however,this algorithm’performance will decrease.This paper introduces a new BSS approach for source noise,which is achieved by firstly calculating the Wigner TFD matrix of observed signals,and uses the Hough transform to convert the signals detection to find the local peak values in the parameter space through considering the TFD as an image,followed by joint diagonalization of a combined set of matrix selected by auto-term theory,to estimate the source signals.This method extends the BSS constraints,and increases the robustness by spreading the noise power while localizing the source energy in the time-frequency domain.
Keywords:source noise  Wigner distribution  Hough transform  blind source separation
本文献已被 CNKI 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号