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A robust blind source separation algorithm based on generalized variance

机译:一种基于广义方差的鲁棒盲源分离算法

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To solve the problem of blind source separation, a robust algorithm based on generalized variance is presented by exploiting the different temporal structure of uncorrelated source signals. In contrast to higher order cumulant techniques, this algorithm is based on second order statistical characteristic of observation signals, can blindly separate super-Gaussian and sub-Gaussian signals successfully at the same time without adjusting the contrast function, and the computation burden of it is relatively light. Simulation results confirm that the algorithm is efficient and feasible.
机译:为了解决盲源分离的问题,通过利用不同的不相关源信号的不同时间结构来呈现基于广义方差的基于广义方差的稳健算法。 与高阶累积技术相比,该算法基于观察信号的二阶统计特性,可以同时盲目地分离超高斯和子高斯信号,而无需调整对比度,并且它的计算负担 相对轻。 仿真结果证实该算法是有效可行的。

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