Off-axis telescope misalignment correction based on defocus spot moment features

Abstract

Optical mirror misalignments, which are caused by assembly mistakes and changes in the surrounding environment (such as gravity, temperature, and atmosphere), degrade the system’s imaging performance. Therefore, active misalignment correction is essential for ensuring the image quality of the off-axis telescope. In this paper, a novel misalignment correction method without wavefront sensors is proposed. The point spread functions (PSFs) of the system are analytically related to the optical mirror misalignments. On this basis, a fully connected neural network (FCNN) is used to establish the mapping relationship between the misalignments and the discrete orthogonal unbiased finite impulse response (UFIR) moment features, which can effectively characterize changes of intensity and geometric of the spot image. The simulation and experimental results in this paper justify the effectiveness and practicality of the proposed method. This approach offers a low-cost and straightforward technical method for achieving high imaging quality throughout the alignment and observation phases. This approach can prevent the accumulation of errors caused by wavefront detection and the high delay of multiple iterations.