Local mean decomposition algorithm improved by de-correlation

Ying Xiao, Yu-Hua Dong
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引用次数: 1

Abstract

To solve the mode mixing problem of local mean decomposition (LMD), hereby a de-correlation improved LMD algorithm was proposed. If the multi-components signal includes two signal components with similar frequency, LMD will produce mode mixing which has serious impact on signal feature extraction and subsequent time frequency analysis. The essence of the mode mixing is that the information of product functions (PF) obtained by LMD mutual coupling each other. That is the PF is incomplete orthogonality. For the zero mean value random signal, the orthogonality and non-correlation are equivalent. By embedding the de-correlation operation in the LMD process, the orthogonality between the PF can be further guaranteed, and the purpose of suppressing the mode mixing is achieved. The simulation results show that the LMD algorithm improved by de-correlation has superior performance in suppressing the mode mixing.
局部均值分解算法的去相关改进
为了解决局部均值分解(LMD)的模态混合问题,提出了一种改进的局部均值分解(LMD)去相关算法。如果多分量信号包含两个频率相近的信号分量,LMD会产生模态混频,严重影响信号特征提取和后续时频分析。模态混合的本质是LMD得到的积函数(PF)信息相互耦合。即PF是不完全正交。对于零均值随机信号,正交性和非相关性是等价的。通过在LMD过程中嵌入去相关运算,进一步保证了PF之间的正交性,达到抑制模混叠的目的。仿真结果表明,通过去相关改进的LMD算法在抑制模式混频方面具有较好的性能。
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