Full-waveform LiDAR echo decomposition method based on deep learning and sparrow search algorithm

Abstract

For the full-waveform LiDAR echo, the conventional decomposition method is to judge the number of waveform components in the echo through the IP (inflection point) method or RL (Richardson-Lucy) deconvolution method after filtering and obtain the initial estimation of their parameters, and then use the intelligent optimization algorithm to fit the waveform. However, this preprocessing method has poor accuracy in judging the echo components when the distance between targets is small. In this paper, a highly accurate decomposition method is proposed based on LSTM with SSA (Sparrow Search Algorithm). Firstly, the LSTM network trained by the simulation data sets under three kinds of background noise is used to judge the number of Gaussian components in the full-waveform LiDAR echo, and then SSA is used for waveform fitting. The accuracy rate of each LSTM network is more than 95%. This method is compared with IP, RL and MGD (multi-Gaussian decomposition) method. Within the accuracy of 0.1 m, the minimum decomposition distance of LiDAR echo is shorten from 0.7 m to 0.45 m compared with IP method and from 0.55 m to 0.45 m compared with RL and MGD method. The minimum ranging distance of LiDAR echo is shorten from 0.75 m to 0.65 m compared with IP and RL method and from 0.85 m to 0.65 m compared with MGD method. With proposed method, the accuracy of full-waveform LiDAR echo decomposition is improved and distance limit of decomposition is reduced.