An EEMD-SVD-LWT algorithm for denoising a lidar signal

A segmentation singular value decomposition (SVD)-lifting wavelet transform (LWT) denoising algorithm based on ensemble empirical mode decomposition (EEMD) was proposed to better suppress noise in an atmospheric lidar return signal. The EEMD method is used to distinguish inherent modal functions (IMFs) of the noise and signal, and remove the IMF with noise as its main component. Moreover, the SVD-LWT method is adopted to remove the noise in the IMF component containing the signal and thus finely extract the signal. The simulated Bumps signal with different sequences of Gaussian white noise was denoised, and the denoising effect of the EEMD-SVD-LWT algorithm was compared with the effects of the wavelet soft threshold, EEMD (correlation coefficient), and EEMD (difference value) methods. Simulation shows that the denoising effect of the EEMD-SVD-LWT algorithm was best. The EEMD-SVD-LWT algorithm was also used to denoise practical lidar signals and was better than that achieved with the other methods.

Automated summary

A segmentation SVD-LWT denoising algorithm based on EEMD was proposed to better suppress noise in atmospheric lidar return signals, with simulations showing superior results. The algorithm was also applied to denoise practical lidar signals, outperforming other methods.