Lidar denoising algorithm of improved CEEMDAN combined with novel wavelet change
In order to solve the problem of high background noise of the return signals in the long-distance detection of lidar,a lidar denoising algorithm is proposed by combining complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),detrended fluctuation analysis(DFA)and novel wavelet transform.Firstly,the lidar return signal is decomposed by CEEMDAN to obtain multiple intrinsic mode function(IMF).Secondly,the DFA algorithm is introduced to calculate the scaling exponent of each IMF component with respect to the original return signal,adaptively divide the IMF components into signal-dominant components and noise-dominant components.Thirdly,the novel wavelet transform is used to denoise the noise-dominant components.Finally,the signal-dominant components are combined with the denoised noise-dominant components for signal reconstruction.The denoising results of the lidar simulation signal at-10 dB show that compared with the improved CEEMDAN combined with wavelet soft and hard thresholds,the root mean square error of the proposed algorithm has decreased by 52.13％and 96.49％respectively,and the signal to noise ratio has increased by 3.932 3 dB and 3.754 2 dB respectively.The denoising results of the measured signal also indicate that the proposed algorithm has good robustness and better denoising performance under low signal to noise ratio conditions.
lidardenoisingcomplete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)wavelet threshold function
万方数据
维普
