Learning-based surface deformation recovery for large radio telescope antennas

被引:0
作者
Tong, Zhan [1 ]
Ren, Xuesong [1 ]
Meng, Guoxiang [1 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
deep learning; phaseless measurements; phase retrieval; REFLECTOR ANTENNAS; PHASE RETRIEVAL; HOLOGRAPHY; ALGORITHM; FIELD;
D O I
10.1017/S1759078724000217
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The surface deformation of the main reflector in a large radio telescope is closely related to its working efficiency, which is important for some astronomical science studies. Here, we present a deep learning-based surface deformation recovery framework using non-interferometric intensity measurements as input. The recurrent convolutional neural network (RCNN) is developed to establish the inverse mapping relationship between the surface deformation of the main reflector and the intensity images at the aperture plane and at a near-field plane. Meanwhile, a physical forward propagation model is adopted to generate a large amount of data for pre-training in a computationally efficient manner. Then, the inverse mapping relationship is adjusted and improved by transfer learning using experimental data, which achieves a 15-fold reduction in the number of training image sets required, which is helpful to facilitate the practical application of deep learning in this field. In addition, the RCNN model can be trained as a denoiser, and it is robust to the axial positioning error of the measuring points. It is also promising to extend this method to the study of adaptive optics.
引用
收藏
页码:935 / 945
页数:11
相关论文
共 50 条
[41]   Throughput-enhanced FTIR spectrometers with deep learning-based spectral recovery [J].
Wang, Huijie ;
Yang, Zichun ;
Shang, Linwei ;
Wu, Jinjin ;
Wu, Qingxia ;
Huang, Lang ;
Yin, Jianhua .
INFRARED PHYSICS & TECHNOLOGY, 2024, 136
[42]   Learning-Based Efficient Sparse Sensing and Recovery for Privacy-Aware IoMT [J].
Wei, Tiankuo ;
Liu, Sicong ;
Du, Xiaojiang .
IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (12) :9948-9959
[43]   Deep Learning-Based Spectrum Sensing in Cognitive Radio: A CNN-LSTM Approach [J].
Xie, Jiandong ;
Fang, Jun ;
Liu, Chang ;
Li, Xuanheng .
IEEE COMMUNICATIONS LETTERS, 2020, 24 (10) :2196-2200
[44]   A NEW APPROACH TO PREDICT RADIO MAP VIA LEARNING-BASED SPATIAL LOSS FIELD [J].
Tan, Zhiqiang ;
Yao, Zhiwei ;
Xiaot, Limin ;
Zhao, Ming ;
Li, Yunzhou .
2024 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING WORKSHOPS, ICASSPW 2024, 2024, :770-774
[45]   Reinforcement learning-based clustering protocols for a self-organising cognitive radio network [J].
Ramli, Aizat ;
Grace, David .
TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2016, 27 (04) :544-556
[46]   A Deep Learning-based Approach to 5G-New Radio Channel Estimation [J].
Zimaglia, Elisa ;
Riviello, Daniel G. ;
Garello, Roberto ;
Fantini, Roberto .
2021 JOINT EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS & 6G SUMMIT (EUCNC/6G SUMMIT), 2021, :78-83
[47]   A software-defined radio testbed for deep learning-based automatic modulation classification [J].
Ponnaluru, Sowjanya ;
Penke, Satyanarayana .
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, 2020, 33 (15)
[48]   Deep Learning-Based Dispersion Spectrum Inversion for Surface Wave Exploration [J].
Gan, Yuandi ;
Yang, Zhentao ;
Pan, Lei ;
Sun, Yao-Chong ;
Zhang, Dazhou ;
Gao, Yuqiu ;
Chen, Xiaofei .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62 :1-11
[49]   Development of a Deep Learning-Based Model for Pressure Injury Surface Assessment [J].
Liu, Ankang ;
Ma, Hualong ;
Zhu, Yanying ;
Wu, Qinyang ;
Xu, Shihai ;
Feng, Wei ;
Liang, Haobin ;
Ma, Jian ;
Wang, Xinwei ;
Ye, Xuemei ;
Liu, Yanxiong ;
Wang, Chao ;
Sun, Xu ;
Xiang, Shijun ;
Yang, Qiaohong .
JOURNAL OF CLINICAL NURSING, 2025,
[50]   A Deep Learning-Based Surface Defect Inspection System for Smartphone Glass [J].
Go, Gwang-Myong ;
Bu, Seok-Jun ;
Cho, Sung-Bae .
INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2019, PT I, 2019, 11871 :375-385