PCA优化CEEMD的DSQ水管倾斜仪信号随机噪声压制方法OA北大核心CSTPCD

We propose a random noise suppression method CEEMD-PCA based on principal component analy-sis(PCA)optimized complementary ensemble empirical mode decomposition(CEEMD)for DSQ water tube tiltmeter signal.The method incorporates eight IMF component quality evaluation indexes,such as correlation coefficient,distribution entropy,MSE,R2,SSE,RMSE,MAE,MAPE;it implements dimensionality re-duction and compression of the index value matrix with the help of principal component analysis to transform it into a new parameter that can represent the characteristics of all different types of indexes,and constructs a comprehensive IMF component quality evaluation function to complete the original noise-containing signal ac-cording to the score ranking results.We complete the linear reconstruction of the original noisy signal accord-ing to the score ranking results.The results of both simulated and measured signal denoising experiments show that the CEEMD-PCA model outperforms the classical models such as Kalman filter,70 th-order low-pass FIR filter,etc.,improves the signal-to-noise ratio of the original signal,and accurately completes the signal reconstruction,which can better retain the effective components.