Research on subaperture stitching optimization algorithm for large flat mirror testing

To address the problem of high-precision detection of large flat aperture mirror shapes, the current subaperture stitching methods are analyzed. The results show that the full aperture surface shape stitching errors due to subaperture adjustment errors and localization errors are the main reasons affecting the high precision detection. To improve the detection accuracy of subaperture stitching, we propose an immune optimization algorithm for subaperture stitching. The algorithm reduces the influence of subaperture adjustment errors and positioning errors, so that the PV value of reconstructed shape reaches λ/100. It is shown that the proposed method can effectively control the stitching errors and improve the stitching accuracy.