High-precision aberration retrieval with a scanning quadrant detector and gradient-based evolutionary particle swarm optimization algorithm

During the application of the quadrant detector (QD) system, the performance degradation of the optomechanical structure due to long-term application leads to aberrations in the QD optical system, causing spot distortion and then reducing the QD detection accuracy. Therefore, monitoring the imaging quality of the QD optical system is crucial, as it serves as the key prerequisite for further calibration of QD detection errors. Additionally, phase retrieval and calibration techniques for optical systems provide novel approaches for both post-assembly detection of optomechanical systems and monitoring the optical system's on-orbit status. In this study, based on the high stability of the self-calibrated light source inside the system, a method of inversion spot energy distribution based on QD scanning is proposed for the first time to our knowledge, and a hybrid EPSO-SPGD phase diversity algorithm is constructed by combining the evolutionary particle swarm optimization algorithm (EPSO) and stochastic parallel gradient descent (SPGD) algorithm to realize the retrieval of aberration of a QD optical system. Simulation and experimental results show that the proposed method exhibits high accuracy and robustness under different noise levels. Under specific conditions, the relative error of phase retrieval is below 2.5%, demonstrating a significant performance advantage over traditional algorithms. This provides a reliable and effective technical solution for the health monitoring of QD optical systems.

Authors

Publication Details

Journal
Applied Optics
Published
2025-04-07
DOI
https://doi.org/10.1364/ao.558453
Primary Topic
Optical Systems and Laser Technology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

High-precision aberration retrieval with a scanning quadrant detector and gradient-based evolutionary particle swarm optimization algorithm

Yatian Li, Zhanwei Liu, Shijie Gao, Fangxu Lv et al.
1 links
Applied Optics
Optical Systems and Laser Technology
article

High-precision aberration retrieval with a scanning quadrant detector and gradient-based evolutionary particle swarm optimization algorithm

Yatian Li, Zhanwei Liu, Shijie Gao, Fangxu Lv, Hi Chang Chai, Shiu Fai Li, Zhiyong Wu, Ruipeng Li, Yongkai Liu, Zuoyu Liu
article en

Abstract

During the application of the quadrant detector (QD) system, the performance degradation of the optomechanical structure due to long-term application leads to aberrations in the QD optical system, causing spot distortion and then reducing the QD detection accuracy. Therefore, monitoring the imaging quality of the QD optical system is crucial, as it serves as the key prerequisite for further calibration of QD detection errors. Additionally, phase retrieval and calibration techniques for optical systems provide novel approaches for both post-assembly detection of optomechanical systems and monitoring the optical system's on-orbit status. In this study, based on the high stability of the self-calibrated light source inside the system, a method of inversion spot energy distribution based on QD scanning is proposed for the first time to our knowledge, and a hybrid EPSO-SPGD phase diversity algorithm is constructed by combining the evolutionary particle swarm optimization algorithm (EPSO) and stochastic parallel gradient descent (SPGD) algorithm to realize the retrieval of aberration of a QD optical system. Simulation and experimental results show that the proposed method exhibits high accuracy and robustness under different noise levels. Under specific conditions, the relative error of phase retrieval is below 2.5%, demonstrating a significant performance advantage over traditional algorithms. This provides a reliable and effective technical solution for the health monitoring of QD optical systems.

Applied OpticsVol. 64(13)
Openalex Percentile: Top 95%
Optical Systems and Laser Technology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.