Wang, Peng-Fei; Zhao, Hui; Xie, Xiao-Peng; Zhang, Ya-Ting; Li, Chuang; Fan, Xue-Wu
To realize the large-scale and high-precision co-phasing adjustment of synthetic-aperture telescopes, we propose a multichannel left-subtract-right feature vector piston error detection method based on a convolutional neural network, which inherits the high precision and strong noise resistance of the DFA-LSR method while achieving a detection range of (-139 lambda, 139 lambda) (lambda = 720 nm). In addition, a scheme to build large training datasets was proposed to solve the difficulty in collecting datasets using traditional neural network methods. Finally, simulations verified that this method can guarantee at least 94.96% accuracy with large samples, obtaining a root mean square error of 10.2 nm when the signal-to-noise ratio is 15.
The result was published on OPTICS EXPRESS. DOI: 10.1364/OE.428690
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