针对盲信号抽取问题,根据极大似然估计原理,给出一种基于极大似然估计的分步盲抽取算法。根据信号不同的延时特性确立抽取信号,完成第一步抽取。由于初步抽取信号存在噪声污染,根据极大似然估计,估计抽取信号的概率密度函数,确立优化代...针对盲信号抽取问题,根据极大似然估计原理,给出一种基于极大似然估计的分步盲抽取算法。根据信号不同的延时特性确立抽取信号,完成第一步抽取。由于初步抽取信号存在噪声污染,根据极大似然估计,估计抽取信号的概率密度函数,确立优化代价函数,利用自然梯度方法进行优化,确立最终抽取向量迭代方式,完成对抽取信号优化处理。通过仿真证明算法具有良好的收敛性和抗噪性,在SNR>17 d B时,抽取信号与源信号的相似度达到95%。展开更多
In this paper,X is a locally compact Hausdorff space and A is a Banach algebra.First,we study some basic features of C0(X,A)related to BSE concept,which are gotten from A.In particular,we prove that if C0(X,A)has the ...In this paper,X is a locally compact Hausdorff space and A is a Banach algebra.First,we study some basic features of C0(X,A)related to BSE concept,which are gotten from A.In particular,we prove that if C0(X,A)has the BSE property then A has so.We also establish the converse of this result,whenever X is discrete and A has the BSE-norm property.Furthermore,we prove the same result for the BSE property of type I.Finally,we prove that C0(X,A)has the BSE-norm property if and only if A has so.展开更多
Blind source extraction （BSE） is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive B...Blind source extraction （BSE） is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive BSE algorithm with an additive noise model. We first present an improved normalized kurtosis as an objective function, which caters for the effect of noise. By combining the objective function and Lagrange multiplier method, we further propose a robust algorithm that can extract the desired signal as the first output signal. Simulations on both synthetic and real biomedical signals demonstrate that such combination improves the extrac- tion performance and has better robustness to the estimation error of normalized kurtosis value in the presence of noise.展开更多
文摘针对盲信号抽取问题,根据极大似然估计原理,给出一种基于极大似然估计的分步盲抽取算法。根据信号不同的延时特性确立抽取信号,完成第一步抽取。由于初步抽取信号存在噪声污染,根据极大似然估计,估计抽取信号的概率密度函数,确立优化代价函数,利用自然梯度方法进行优化,确立最终抽取向量迭代方式,完成对抽取信号优化处理。通过仿真证明算法具有良好的收敛性和抗噪性,在SNR>17 d B时,抽取信号与源信号的相似度达到95%。
文摘In this paper,X is a locally compact Hausdorff space and A is a Banach algebra.First,we study some basic features of C0(X,A)related to BSE concept,which are gotten from A.In particular,we prove that if C0(X,A)has the BSE property then A has so.We also establish the converse of this result,whenever X is discrete and A has the BSE-norm property.Furthermore,we prove the same result for the BSE property of type I.Finally,we prove that C0(X,A)has the BSE-norm property if and only if A has so.
文摘Blind source extraction （BSE） is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive BSE algorithm with an additive noise model. We first present an improved normalized kurtosis as an objective function, which caters for the effect of noise. By combining the objective function and Lagrange multiplier method, we further propose a robust algorithm that can extract the desired signal as the first output signal. Simulations on both synthetic and real biomedical signals demonstrate that such combination improves the extrac- tion performance and has better robustness to the estimation error of normalized kurtosis value in the presence of noise.
