大规模自适应光学系统要求哈特曼-夏克波前传感器的子孔径数目巨大，这势必会增加波前斜率提取电路实现的难度。为了在大规模自适应光学系统中进行波前斜率提取，提出了一种相关算法的实现结构，以高斯光斑作为参考模板，通过两次级联滤波完成子孔径光斑之间的相关运算，并得到局部波前斜率。与多通道并行处理的实现结构相比，本文所提出的结构实现的硬件成本不会随着子孔径规模的增大而显著增加，特别适用于大规模自适应光学系统的波前斜率提取。实验结果表明：对于8×8 方形排布的哈特曼-夏克波前传感器图像，本文的结构用FPGA 内实现，在27 MHz 的工作频率下，完成一帧所有子孔径斜率计算的延迟为1.2 μs，均方根误差小于0.02个像素，最大误差不超过0.04 个像素。而资源消耗仅仅为925 个Slices。本结构能够满足大规模自适应光学系统中波前斜率探测高速实时和高精度要求。

Adaptive optics systems with larger size require more subapertures in the Shack-Hartmann wavefront sensor, which makes it harder to compute the wavefront gradient in real time. In order to measure local gradients in large-size adaptive optics systems, an architecture based on cross correlation is proposed. Using the Gaussian spot as the reference template, the correlation between subaperture spots is calculated by means of two cascading filters, and then the local wavefront gradient of each subaperture is determined. Compared with the existing parallel processing approach in correlating-Hartmann sensor, the cost of our architecture will not increase greatly as subapertures multiplies, which makes it more suitable for adaptive optics systems of extreme size to retrieve gradients. Experimental results indicate that when implemented in Field Programmable Gate Array (FPGA) at 27 MHz, our design can obtain gradients of all subapertures in 8×8 Shack-Hartmann with latency no more than 1.2 μs, while the RMS error is less than 0.02 pixels, and the maximum error does not exceed 0.04 pixels. It can satisfy the requirements of low cost, real time and high accuracy for gradient detection in adaptive optics systems with large-size aperture.