Optimal VMD-Based Signal Denoising for Laser Radar via Hausdorff Distance and Wavelet Transform

Laser radar echo signals are easily contaminated by noise, such as background light and electronic noise, and this noise is an obstacle for the subsequent signal detection. However, the conventional denoising methods cannot achieve satisfactory effects when the signal-to-noise-ratio (SNR) is ultralow. In this paper, a novel denoising method for laser radar echo signals based on the parameter-optimal variational mode decomposition (VMD) combined with the Hausdorff distance (HD) and wavelet transform (WT) is proposed. Compared with conventional VMD-based methods, the proposed method utilizes a newly developed grasshopper optimization algorithm (GOA) to obtain the optimal combination of parameters for the VMD. Then, the HD is applied to select the relevant modes and then uses the basis function to reconstruct the signal. In addition, the relevant modes are further processed by the WT denoising method, which allows the reconstructed signal to obtain a higher SNR. The simulation and experimental results show the feasibility, effectiveness and robustness of the proposed method compared to three other available denoising techniques. The proposed method could promote the distance measurement performance of laser radars in harsh environments.

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