Co-phase errors sensing method for Golay3 telescope system via a transfer network.

Optical sparse-aperture systems face severe challenges, including detecting and correcting co-phase errors. In this study, a search framework based on fine tuning a pre-trained network is proposed to analyze the co-phase errors of a Golay3 telescope system. Based on this, an error compensation control system is established. First, a hash-like binary code is created by fine-tuning the pre-trained model. Secondly, a pre-trained network is used to extract the deep features of the image, and an index database is built between the image features and the corresponding co-phase error values. Finally, the Top 1-ranked features and corresponding co-phase error values are returned through the hash-like binary code hierarchical deep search database to provide driving data for the error correction system. Numerical simulations and experimental results verify the method's validity. The experimental results show that the correction system works well when the dynamic piston is [-5,5]λ, and the tilt error range is [-15,15]µr a d. Compared with existing detection methods, this method does not require additional optical components, has a high correction accuracy, and requires a short training time. Furthermore, it can be used to detect piston and tilt errors simultaneously.