Atmospheric turbulence originates from random fluctuations in the refractive index of the propagation medium that induce wavefront distortions and intensity scintillation. In application scenarios such as adaptive optics, rapid and accurate characterization of turbulence conditions is of critical importance. Existing turbulence-sensing approaches predominantly rely on intensity statistical analysis, wavefront measurements, and parameter estimation inferred from imaging degradation. However, these methods typically require complex reconstruction procedures, leading to increased system complexity and substantial computational overhead, which limits their applicability in scenarios demanding low-latency lightweight architectures, such as adaptive optics and ground-to-satellite laser communications. In this work, turbulence perception is reformulated from a conventional wavefront reconstruction problem into a measurement-operator design problem. We propose an all-optical turbulence perception framework based on a multilayer diffractive processor. The proposed approach maps the phase statistical characteristics induced by atmospheric turbulence into discriminative intensity-domain features, enabling direct perception of turbulence strength. The perception process is performed exclusively in the optical domain, without the need for numerical reconstruction. Numerical results demonstrate that the proposed diffractive processor can robustly distinguish different turbulence strength levels, with an overall classification accuracy of 79.50%, indicating its effectiveness as a new technological pathway for atmospheric turbulence perception.
Ma et al. (Thu,) studied this question.
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