An improved Gaussian laser beam probability distribution simulation based on Monte Carlo method

Abstract

Research on target recognition in random media by Monte Carlo method has made rapid progress. However, the commonly used probability sampling function of the emitted photons’ directions is not suitable for simulating the radial cross-sectional distribution of a beam. This sampling has little effect on the simulated laser transmission in clouds, but if the laser range profile (LRP) of a target is simulated, it will cause serious distortion because the common sampling method cannot well represent the radial two-dimensional intensity distribution of the beam. In this paper, the traditional sampling method is improved through rigorous derivation, and the superiority of the method is illustrated by simulation data. The simulation results show that the Monte Carlo model of LRP based on the improved sampling method plays well in profile shape of ideal targets identification. This research can bring more reference and significance to target recognition application.