Fig. 1. (a) Schematic of the distorted grating and (b) geometric relationship of the ±1st diffraction order imaging positions.

Fig. 2. Schematic of target-independent wavefront sensing.

Fig. 3. Structure of the AM-EffNet for target-independent wavefront sensing.

Fig. 4. (a) Time domain feature. The red boxes in (b) are the enumerated power features, and the blue boxes in (c) are the sharpness features. We can see that the normalized fine feature in (d) includes the information of the sharpness and the power features with data distributed between 0 and 1. (e) Comparison of loss for different features in training.

Fig. 5. Residual wavefront at different defocus amount. The letters A, B, C, D, and E in the abscissa express gratings with different defocusing degrees, and the numbers 1, 2, and 3 represent three different degrees of atmospheric turbulence in the range of 0–0.5λ, 0.5λ–1.0λ, and 1.0λ–1.5λ of the original wavefront RMS, respectively. The red circles represent outliers.

Fig. 6. A group of wavefront sensing results of targets in five different scenarios with our method. The RMSE/Truth represents the ratio of the residual wavefront to the true wavefront.

Table 1. Testing Results Based on Our Method