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A New Algorithm to Estimate Mixing-Matrix of Underdetermined Blind Signal Separation

机译:一种估计未确定盲信号分离混合矩阵的新算法

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The paper puts forward a new algorithm to estimate mixing-matrix according to the underdetermined blind signal separation of 3 observed signals and 4 sources. According to the geometric meaning of the SCA model, the paper analyzes the numerical feature of the observed signal and proves that the inner product under Euclidean space can be used to classify the observed signal in the situation. Besides, the paper gives a method for determining the number of source signal and introduces an estimation algorithm for mixing-matrix using inner products in the Euclidean space combined with the density of interval point. The algorithm can effectively identify the number of source signals and can realize the estimation of mixing-matrix. The experimental results show the algorithm is feasible.
机译:本文提出了一种新的算法根据3观察到的信号和4个来源的未定定的盲信号分离来估计混合矩阵。 根据SCA模型的几何含义,本文分析了观察信号的数值特征,并证明了欧几里德空间下的内部产品可用于在情况下对观察到的信号进行分类。 此外,本文给出了一种用于确定源信号的数量的方法,并使用欧几里德空间中的内部产品结合间隔点的密度来引入混合矩阵的估计算法。 该算法可以有效地识别源信号的数量,并且可以实现混合矩阵的估计。 实验结果表明该算法是可行的。

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