基于聚类最小二乘法和斜率相结合的极值点延拓方法

针对经验模态分解法(empirical mode decomposition,EMD)对信号进行去噪处理中可能产生的端点效应问题,提出一种聚类最小二乘与斜率相结合的极值点延拓方法。该方法充分考虑了噪音对延拓方法的影响,利用聚类最小二乘法描述全局极值点的变化趋势,并结合信号的边界局部特征来确定延拓点的坐标信息。通过对仿真信号和陀螺仪实例信号展开研究,利用相似系数、均方根误差和正交性水平等指标对算法性能进行评价。实验结果表明:本文提出的方法可以有效的抑制EMD端点效应,最大程度地避免了端点效应对中间数据的污染,从而提高了信号去噪的准确性和可靠性,为EMD在信号处理中的应用提供了一个可行而有效的解决方案。

Extreme point continuation method based on clustering least square method and slope

The method of extreme point extension based on clustering least squares and slope was proposed to address the endpoint effect that could occur in signal denoising by empirical mode decomposition (EMD). This method fully considered the influence of noise on the continuation method, described the changing trend of global extreme points using the clustering least square method, and determined the coordinate information of the continuation points by combining the local characteristics of the signal boundary. The performance of the algorithm was evaluated by similarity coefficient, root mean square error and orthogonality level through the study of simulation signal and gyro example signal. The experimental results show that the method proposed in this paper can effectively suppress the endpoint effect of EMD and avoid the pollution of the endpoint effect to the intermediate data to the greatest extent, thus improving the accuracy and reliability of signal denoising, and providing a feasible and effective solution for the application of EMD in signal processing.