Yue, D.; Song, P.; Wang, C.; Chuai, Y. Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network. Sensors2023, 23, 8399.
Yue, D.; Song, P.; Wang, C.; Chuai, Y. Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network. Sensors 2023, 23, 8399.
Yue, D.; Song, P.; Wang, C.; Chuai, Y. Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network. Sensors2023, 23, 8399.
Yue, D.; Song, P.; Wang, C.; Chuai, Y. Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network. Sensors 2023, 23, 8399.
Abstract
Aimed for the difficulty and complexity of detecting the piston error for segmented telescope, this paper proposed a new piston error measurement method based on a hybrid artificial neural net-work. Firstly, we use Resnet network to learn the mapping relationship between the focal plane degradation image and the signs of the piston error. Then, based on the established theoretical re-lationship between modulation transfer function and the piston error, BP neural network is used here to learn the mapping relationship between the MTF and the absolute value of the piston error. After the training of the hybrid network is completed, a wide-range and high-precision detection of the piston error of the sub-mirrors can be achieved using the combined output of the two net-works only a focal plane image of a point source with broadband illumination is used as input. The detection range can reach the whole coherent length of the input broadband light, and the detec-tion accuracy can reach 10nm. The method proposed in this paper has the advantages of high de-tection accuracy, wide detection range, low hardware cost, small network scale and low training difficult.
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