Research on Wind Vector Measurement of Coherent Doppler Lidar Based on Particle Swarm Optimization Algorithm

Coherent Doppler wind Lidar (CDWL) is a lidar based meteorological detection device, mainly used to measure wind speed and direction in the atmosphere. Peak value extraction is a very important step in wind field inversion of CDWL. It is a difficult problem to extract peak value from many CDWL raw data. To solve this problem, a particle swarm optimization (PSO) algorithm is proposed in this paper to process CDWL raw data. PSO algorithm is simple and easy to implement, can be parallelized, has strong adaptability, and has excellent global search ability, fast convergence ability and strong robustness of population optimization algorithm. Therefore, the PSO algorithm has advantages in extracting Doppler peaks from large amounts of raw data. The PSO algorithm is applied to CDWL time domain simulation signal and measured data processing, compared with other peak extraction methods. The results show that the average peak accuracy of PSO algorithm reaches 0.992. Compared with the wind vector fitted by other peak extraction methods, the PSO algorithm has no error within the detection distance of 2000 meters, and can detect further and more accurate wind vectors, improving the accuracy and reliability of data analysis