Li Dequan, Wang Dong, Yan Dejie
Space Optics Department, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
Sensors (Basel). 2024 Jun 29;24(13):4236. doi: 10.3390/s24134236.
The segmented mirror co-phase error identification technique based on supervised learning methods has the advantages of simple application conditions, no dependence on custom sensors, a fast calculation speed, and low computing power requirements compared with other methods. However, it is often difficult to obtain a high accuracy in practical application situations with this method because of the difference between the training model and the actual model. The reinforcement learning algorithm does not need to model the real system when operating the system. However, it still retains the advantages of supervised learning. Thus, in this paper, we placed a mask on the pupil plane of the segmented telescope optical system. Moreover, based on the wide spectrum, point spread function, and modulation transfer function of the optical system and deep reinforcement learning-without modeling the optical system-a large-range and high-precision piston error automatic co-phase method with multiple-submirror parallelization was proposed. Finally, we carried out relevant simulation experiments, and the results indicate that the method is effective.
基于监督学习方法的分块镜共相位误差识别技术与其他方法相比，具有应用条件简单、不依赖定制传感器、计算速度快和计算能力要求低等优点。然而，由于训练模型与实际模型存在差异，该方法在实际应用场景中往往难以获得高精度。强化学习算法在操作系统时无需对实际系统进行建模。然而，它仍然保留了监督学习的优点。因此，在本文中，我们在分块望远镜光学系统的光瞳平面上放置了一个掩膜。此外，基于光学系统的宽光谱、点扩散函数和调制传递函数以及深度强化学习——不对光学系统进行建模——提出了一种多子镜并行化的大范围、高精度活塞误差自动共相位方法。最后，我们进行了相关的仿真实验，结果表明该方法是有效的。
