Prediction of laser far-field intensity distribution through passive imaging

The far-field intensity of lasers is an essential parameter for long-range laser emission, affecting the performance in laser countermeasures, laser imaging, and laser communication, etc. Predicting the laser distribution and power density before actual emission is therefore crucial. During long-distance air transmission, the far-field intensity distribution is influenced by atmospheric factors such as scattering and turbulence. The conventional calculation method utilizes separate visibility and turbulence testing equipment to abstract the current atmospheric conditions into a few parameters, followed by the application of physical formulas or empirical rules to estimate a result, which causes the loss of detailed atmospheric information. This paper presents a novel method for predicting intensity distribution. By performing analysis on the image acquired from the light path of the laser emission system, we can extract the atmospheric factors and predict the laser distribution using deep learning models, thereby avoiding the need for additional equipment and enabling quasi-real-time prediction. Simulation and experiments demonstrate that the new method achieves higher accuracy and has the potential to provide computation results for in-time decision.