Signal Denoising Method Combined With Variational Mode Decomposition, Machine Learning Online Optimization and the Interval Thresholding Technique

Abstract

The signal-to-noise ratio of lidar signals decreases rapidly with an increase in distance, which seriously affects the application of lidar detection technology. Variational mode decomposition (VMD) has performed optimality in dealing with noise, but the number of modes, K, and the penalty parameter, α, must be preset. Therefore, a novel lidar signal denoising method that combines VMD with machine learning online optimization (MLOO) and the interval thresholding (IT) technique, named VMD-MLOO-IT, is proposed in this article. The proposed method defines new fitness functions to evaluate the result of VMD-based denoising, and selects the optimal parameters by the model which development by MLOO. In addition, IT is used to deal with the recovered signal. The experimental results demonstrate the superiority of the presented method over the other empirical mode decomposition-based and VMD-based denoising methods.

Keywords

• LiDAR signal denoising
• machine learning online optimization
• Gaussian process
• variational mode decomposition
• fitness function