Enhancement method of weak Lidar signal based on adaptive variational modal decomposition and wavelet threshold denoising

Abstract

Aiming to identify and detect the weak signal of lidar, a cascade method of adaptive variational modal decomposition (VMD) and wavelet threshold (WT) denoising is proposed in this paper. Firstly, VMD is used to decompose the echo signal, and the criterion of adaptive mode selection is improved. Furthermore, the “db4” wavelet and soft threshold denoising method are used to process the denoised signal by AVMD. The optimal threshold value is obtained by minimizing Stein's unbiased risk estimator. In order to verify the superity of the proposed method, Bumps signal is employed in simulation. When the input signal-to-noise ratio (SNR) is −3dB, the SNR of proposed method can reach 7.8587 dB. Compared with three methods, the SNR of proposed method is the highest under different input SNRs. The real Lidar signal is recorded to be denoised. The root mean square errors of WT-db4, EMD-DT, SVD-WT and the proposed method are 0.0046, 0.0044, 0.0044 and 0.0043. Compared with three state-of-art denoising methods, the proposed denoising method can effectively eliminate the spike noise and fully retain the echo signal.