低采样率下经验模态分解性能提升研究

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摘要经验模态分解(EMD)使用信号极值点的位置和取值信息进行分解，对采样率有较高的要求。针对EMD在低采样率下性能降低的现象，提出一种基于B样条拟合的信号局部均值计算方法。首先提取信号极值点出现的时刻作为尺度，然后通过对极值点时刻进行重新采样构造B样条节点，最后应用B样条最小二乘拟合方法直接计算局部均值。与EMD方法相比，本文方法不需要信号极值点的准确位置和取值，因此不容易受到低采样率的影响。对平稳信号和非平稳信号的仿真结果表明，该方法能在接近奈奎斯特频率的低采样率下获得较高的性能。与基于插值的解决方案相比，该方法的分离性能更好。

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	黎恒1
	李智2
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	莫玮1

关键词 ：经验模态分解, B样条拟合, 低采样率, 信号分解, 时频分析

Abstract：Empirical mode decomposition (EMD) depends highly on the exact location and value of extrema, which requires a high degree of oversampling. Aiming at improving the performance of EMD under low sampling rates, a local mean estimation method based on B-spline approximation is proposed. Firstly, the location of extrema is extracted as the time scale. Then, the location is re-sampled to generate knots for B-splines. Finally, the local mean is computed directly based on B-spline least squares approximation. In compare with the existing EMD method, the exact location and value of extrema are not essential to the proposed technique. The efficiency of this technique is demonstrated using synthetic signals. Experiments show that the performance of the proposed method is not reduced even the sampling rate is close to the Nyquist rate. It also demonstrates that the proposed method is superior to existing interpolation methods in separation performance.

Key words： empirical mode decomposition B-spline approximation low sampling rates signal decomposition time-frequency analysis

收稿日期: 2015-04-07 出版日期: 2016-08-25

引用本文:

黎恒1，李智2,3，莫玮1. 低采样率下经验模态分解性能提升研究[J]. 振动与冲击, 2016, 35(17): 185-190.
LI Heng1, LI Zhi2,3, and MO Wei1. Performance improvement for empirical mode decomposition at low sampling rates. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(17): 185-190.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2016/V35/I17/185

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