Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer

Abstract

Lidar is important active remote sensing equipment in the field of atmospheric environment detection. However, the detection range of lidar is severely limited by the dynamic range of photodetectors. To solve this problem, atmospheric lidars are often equipped with two or more channels to receive signals from different altitude ranges, where gluing the multi-channel echo signals becomes a key issue for accurate data inversion. In this paper, a multi-channel signal gluing algorithm based on the Improved Gray Wolf Optimizer (IGWO) and Neighborhood Rough Set (NRS), named IGWO-RSD, is proposed. The fitness function F is formed by three objective functions: correlation coefficient R, regression stability coefficient S and mean fit deviation D. All three objective functions are obtained from the data itself and do not rely on prior information. The weights of the objective functions R, S and D are pre-trained by NRS, and IGWO is used to optimize the fitness function F. With ground-based aerosol lidar data, all-day signal gluing experiments are performed, where IGWO-RSD demonstrates obvious advantages in stability, accuracy and applicability in lidar signal processing compared with NRSWNSGA-II.