Error Analysis and Eliminate Methods for Wavefront Detection Based on Phase Diversity Algorithm

Dan, 岳丹 Yue; Shuyan, 徐抒岩 Xu; Haitao, 聂海涛 Nie; Hongcai, 马宏财 Ma; Nannan, 刘南南 Liu

doi:10.3788/aos201636.0111003

Cited by 3 publications

(1 citation statement)

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“…However, these abovementioned wavefront detection techniques need to introduce new optical hardware equipment to the original optical system, which heavily increases the complexity of the optical path. The wavefront detection technology based on the focal plane image mainly includes phase retrieval [11,12] and the phase diversity algorithm [13,14], and has low hardware costs, since no other hardware equipment is needed. These methods have high precision but narrow detection ranges, which are not suitable for detecting large piston errors.…”

Section: Of 19mentioning

confidence: 99%

Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network

Yue,

Song,

Wang

et al. 2023

Sensors

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To address the difficulty and complexity of detecting piston errors for segmented telescopes, this paper proposes a new piston error measurement method based on a hybrid artificial neural network. First, we use the Resnet network to learn the mapping relationship between the focal plane degradation image and signs of the piston error. Then, based on the established theoretical relationship between the modulation transfer function and the piston error, a BP neural network is used 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 networks, where only a focal plane image of the point source with broadband illumination is used as the input. The detection range can reach the entire coherent length of the input broadband light, and the detection accuracy can reach 10 nm. The method proposed in this paper has the advantages of high detection accuracy, a wide detection range, low hardware cost, a small network scale, and low training difficulty.

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Section: Of 19mentioning

confidence: 99%

Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network

Yue,

Song,

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Defocused spatial diversity technology with aperture scanning for aberration detection and image restoration in aberrated imaging systems

Dong

et al. 2018

Opt. Eng.

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Piston Error Measurement for Segmented Telescope Based on a Hybrid Artificial Neural Network

Yue,

Song,

Wang

et al. 2023

Preprint

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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.

show abstract

Error Analysis and Eliminate Methods for Wavefront Detection Based on Phase Diversity Algorithm

Cited by 3 publications

References 0 publications

Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network

Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network

Defocused spatial diversity technology with aperture scanning for aberration detection and image restoration in aberrated imaging systems

Piston Error Measurement for Segmented Telescope Based on a Hybrid Artificial Neural Network

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