Simulating anisoplanatic turbulence by sampling intermodal and spatially correlated Zernike coefficients

Simulating atmospheric turbulence is an essential task for evaluating turbulence mitigation algorithms and training learning-based methods. Advanced numerical simulators for atmospheric turbulence are available, but they require evaluating wave propagation, which is computationally expensive. We present a propagation-free method for simulating imaging through turbulence with applications in ground-to-ground imaging. The key idea behind our work is a method to draw intermodal and spatially correlated Zernike coefficients. By establishing the equivalence of the angle-of-arrival correlation by Basu, McCrae, and Fiorino (2015) with the multiaperture correlation by Chanan (1992), we show that the Zernike coefficients can be drawn according to a covariance matrix defining the correlations. We propose fast and scalable sampling strategies to draw these samples. The method allows us to compress the wave propagation problem into a sampling problem, hence making the simulator significantly faster than existing ones. Experimental results show that the simulator has an excellent match with the theory and real turbulence data. We anticipate the simulator, which balances speed and accuracy, will be a useful tool for various computational imaging applications.