Noise Reduction of LiDAR Signal via Local Mean Decomposition Combined With Improved Thresholding Method

The echo signal of light detection and ranging (LiDAR) system is easily disturbed by various noises under the influence of intense background light. The signal to noise ratio (SNR) decreases with increasing distance, which seriously affects the retrieval accuracy of the LiDAR system. This paper combines local mean decomposition and an improved thresholding method (LMD-ITM) to process the Lidar signal with a lot of noise, so as to avoid the loss of useful information. In this research, the correlation coefficient and energy entropy are used to distinguish between the relevant and irrelevant components decomposed by the local mean decomposition. Subsequently, the improved thresholding method is applied to process the irrelevant components. It can extract the effective signals which are difficult to be separated from the high-frequency signals. Finally, all components are reconstructed to get the denoising signal. The experimental results demonstrate that this method can effectively avoid the short-range migration, suppress the long-range noise, and adapt to different weather conditions. Compared with the previous denoising method, the proposed method increases the SNR by about 15%.