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    基于ICA的周期性噪声消除算法

    An Algorithm for Periodic Noise Reduction Based on ICA

    • 摘要: 为了使问题有解 ,传统的独立分量分析算法对问题的条件有许多严格的限制 ,其中包括观测信号的个数不能小于源信号的个数等 在降噪等实际应用中 ,观测信号的个数可能无法满足这一条件 ,为了能够利用独立分量分析分离加性噪声 ,需要人工构造混合信号 基于周期性干扰表现的整体周期性 ,提出了一种构造混合信号的新算法 利用构造的混合信号进行独立分量分析 ,可以有效地消除周期性干扰 ,使目标信号的信噪比显著提高 即使在信噪比很低 ,目标信号几近被“淹没”的情况下 ,仍然能够较好地将其分离出来 该方法具有算法简单、运算速度快、算法效率高等特点 计算机仿真和实验结果都证明了算法的有效性

       

      Abstract: The traditional independent component analysis (ICA) relies on strong assumptions. Among them is the requirement that the number of sensors is more than or equal to that of sources. Thereby, the applications of ICA are limited by the requirement for the number of sources, which might be impractical for noise reduction in many cases. In this paper, a novel method for constructing a mixed signal is proposed based on the holistic periodicity of periodic noises. Much better signal-to-noise ratio (SNR) could be obtained by using ICA with the constructed mixed signal. The algorithm presented proves to be fast, computationally simple and efficient. Simulation and experimental results demonstrate the feasibility and good performance of the proposed algorithm.

       

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