An adaptive continuous threshold wavelet denoising method for LiDAR echo signal

Atmospheric aerosols are the primary contributors to environmental pollution. As such aerosols are micro-to nanosized particles invisible to the naked eye, it is necessary to utilize LiDAR technology for their detection. The laser radar echo signal is vulnerable to background light and electronic thermal noise. While single-photon LiDAR can effectively reduce background light interference, electronic thermal noise remains a significant challenge, especially at long distances and in environments with a low signal-to-noise ratio (SNR). However, conventional denoising methods cannot achieve satisfactory results in this case. In this paper, a novel adaptive continuous threshold wavelet denoising algorithm is proposed to filter out the noise. The algorithm features an adaptive threshold and a continuous threshold function. The adaptive threshold is dynamically adjusted according to the wavelet decomposition level, and the continuous threshold function ensures continuity with lower constant error, thus optimizing the denoising process. Simulation results show that the proposed algorithm has excellent performance in improving SNR and reducing root mean square error (RMSE) compared with other algorithms. Experimental results show that denoising of an actual LiDAR echo signal results in a 4.37 dB improvement in SNR and a 39.5% reduction in RMSE. The proposed method significantly enhances the ability of single-photon LiDAR to detect weak signals.