An efficient recognition method for turbulence intensity via random matrix theory

In this paper, a novel recognition method based on random matrix is proposed for different turbulence intensity. To reflect the degree of atmospheric turbulence, the continuous product form of phase screens is taken into consideration. After calculated the statistical distribution using random matrix theory, the fitting effect of statistical distribution determines the differences of turbulence. Also, based on the data-driven idea, the eigenvalue and singular values of phase screens are described as a whole. According to the adaptability of large dimensional random matrices, the Ring Law and M-P Law breaks through the assumptive restriction of infinite sample, thus building a significant model for potential changes of weak turbulence. The simulation experiments validate that the big data technology is effective attempt for atmospheric turbulence recognition.