Research on Inversion of Lidar Equation Based on Neural Network

Abstract

Abstract. Lidar is an advanced atmospheric and meteorological monitoring instrument. The atmospheric aerosol physical parameters can be acquired through inversion of lidar signals. However, traditional methods of solving lidar equations require many assumptions and cannot get accurate analytical solutions. In order to solve this problem, a method of inverting lidar equation using artificial neural network is proposed. This method is based on BP (Back Propagation) artificial neural network, the weights and thresholds of BP artificial neural network is optimized by Genetic Algorithm. The lidar equation inversion prediction model is established. The actual lidar detection signals are inversed using this method, and the results are compared with the traditional method. The result shows that the extinction coefficient and backscattering coefficient inverted by the GA-based BP neural network model are accurate than that inverted by traditional method, the relative error is below 4%. This method can solve the problem of complicated calculation process, as while as providing a new method for the inversion of lidar equations.

Novel Inversion Algorithm for the Atmospheric Aerosol Extinction Coefficient Based on an Improved Genetic Algorithm

“…In that case, the tutor signal used to construct the network was obtained using the Fernald method. In 2019, Wang et al [22] considered solving nonlinear functions to solve the Mie-scattering lidar equation using the backpropagation neural network. They performed different optimizations to obtain more accurate inversion results without assumptions.…”