摘要
基于稀疏假设,欠定盲源分离问题一般可采用线性规划、最短路径法和组合算法等l1范数最小化方法进行求解,但是这些传统方法对源信号的稀疏性要求较高,从而限制了源信号的估计精度。为此,本文提出了一种改进的l1范数最小化组合算法.该算法根据一定阈值找到与最小l1范数解最接近的若干次优解,将这些次优解和最小l1范数解进行加权叠加,并替代最小l1范数解,作为源信号的估计。采用语音信号的仿真实验表明,对于观测信号个数不太小的高维混合情况,该算法的源信号估计精度能够比传统的l1范数最小化组合算法提高10%左右。
Under the sparse assumption, the problem of underdetermined blind source separation can be solved by l1-norm minimization algorithms such as the linear programming, the shortest-path algorithm, the combinatorial algo- rithm and so on. But these conventional algorithms rely on the high sparseness of the sources, so the recovery accuracy of the sources is not high enough. To overcome this disadvantage, an improved combinatorial algorithm for/l-norm minimization is proposed in this paper. First, the algorithm searches the second best solutions which are close to the minimum l1-norm solution according to a threshold, and then the weighted sum of these second best solutions and the minimum l1-norm solution is taken as the estimation of the sources. The experiments of sound sources show that the recovery accuracy of the sources can be increased by about 10% when the number of mixtures is not too small.
出处
《电子测量与仪器学报》
CSCD
2009年第7期1-5,共5页
Journal of Electronic Measurement and Instrumentation
关键词
欠定盲源分离
稀疏信号
l1范数最小化
线性规划
underdetermined blind source separation
sparse signals
l1-norm minimization
linear programming
