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Enhancing pointing accuracy in Risley prisms through error calibration and stochastic parallel gradient descent inverse solution method

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PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY 2025年 93卷 37-45页

作者： Yuan, Liangzhu Li, Jinying Fan, Yue Shi, Jianliang Huang, Yongmei Chinese Acad Sci Natl Key Lab Opt Field Manipulat Sci & Technol Chengdu 610200 Peoples R China Chinese Acad Sci Key Lab Opt Engn Chengdu 610200 Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu 610200 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Chengdu Univ Sch Mech Engn Chengdu 610106 Peoples R China

High-precision pointing is crucial in evaluating the effectiveness of beam control technology. However, devices utilizing compact rotational Risley prisms (RRP) face a challenge in balancing field-of-view (FOV) and pointing accuracy. This study aims to develop a beam pointing model for RRP and enhance pointing accuracy through error calibration using the system identification method. Moreover, the current two-step inverse solution method is inadequate when the beam deflection model includes the prism's tilt error. To overcome this limitation, this study proposes a stochastic parallel gradient descent (SPGD) inverse solution method to further improve the system's pointing accuracy. A RRP system with a FOV of +/- 15 degrees was constructed for experimentation. The results indicate the pointing accuracy is 2.8 arcsec, the angular dynamic range is 46 dB, and the root-mean-square errors (RMSE) in the x- and y-directions are 1.2 arcsec and 0.9 arcsec, respectively. This device exhibits the highest angular dynamic range among existing RRP devices.

关键词： Pointing Risley prism System identification Inverse solution stochastic parallel gradient descent

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A Novel Sophia-SPGD (stochastic parallel gradient descent) Optimization Method for Wavefront Correction in WFS-Less AO (Wavefront Sensorless Adaptive Optics) Systems

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PHOTONICS 2025年第4期12卷 337-337页

作者： Chen, Peng Yang, Wenjie Ge, Yongqi Zhang, Zhiguang Li, Xianshuo Qi, Zhengqing Yang, Huizhen Jinling Inst Technol Sch Network & Telecom Engn Nanjing 211169 Peoples R China Jiangsu Ocean Univ Sch Elect Engn Lianyungang 222005 Peoples R China

In wavefront sensorless adaptive optics (WFS-less AO) systems, stochastic parallel gradient descent (SPGD) is the primary optimization method for correcting wavefront distortions. However, as the intensity of atmospheric turbulence interference increases, the fixed gain coefficient of the SPGD algorithm results in significant decreases in convergence speed and precision. Moreover, the algorithm is inclined to local optima, thus failing to satisfy the requirements for real-time wavefront distortion correction. To address these issues, this paper introduces a new optimization algorithm, Sophia optimized stochastic parallel gradient descent (Sophia-SPGD), which is based on second-order clipped stochastic optimization in deep learning. This algorithm computes the first-order and second-order moments of the performance metrics from its first and second gradients, respectively, and dynamically modulates the gain via a shearing mechanism to increase the convergence speed and diminish the probability of falling into local optima. Numerical simulations and experiments demonstrate that under strong turbulence conditions, the performance of Sophia-SPGD surpasses that of the traditional SPGD algorithm.

关键词： wavefront sensorless adaptive optics stochastic parallel gradient descent second-order closed stochastic optimization

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Numerical comparison of adaptive optics correction based on stochastic parallel gradient descent and phase conjugate in scintillation conditions

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OPTIK 2020年 208卷 164528-164528页

作者： Ma, Huimin Liu, Haiqiu Zhang, Jinghui Zhang, Pengfei Anhui Agr Univ Coll Informat & Comp Hefei 230036 Peoples R China Chinese Acad Sci Key Lab Atmospher Composit & Opt Radiat Hefei Inst Phys Sci Hefei 230031 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Inst Tech Biol & Agr Engn Hefei 230031 Peoples R China

Adaptive optics (AO) systems with wavefront sensors are based on the phase conjugate (PC) principle. These systems do not work effectively when the scintillation intensity in the receiver aperture becomes significant. AO correction technique based on stochastic parallel gradient descent (SPGD) control algorithm is an alternative approach which does not depend on wavefront sensor but optimizes the performance metric directly. In this study, we established a simulation model for an SPGD-AO system in which the SPGD optimisation algorithm was applied to control the tip-tilt and the deformable mirrors to correct the aberration in several turbulence scenarios. The performances of the SPGD-AO and the PC-AO correction schemes were tested in different scintillation conditions based on numerical simulations of dynamic turbulence. In weak scintillation conditions, the effects of the SPGD-AO correction were as accurate as those of the PC-AO correction, but upon scintillation increasessR > 12, the SPGD-AO yielded a significant correction effect compared to PC correction. The results also showed that SPGD did not require any wavefront phase information, but it was very effective for discontinuous phase correction.

关键词： Adaptive optics stochastic parallel gradient descent Phase conjugate Atmosphere turbulence

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An adaptive enhancement method based on stochastic parallel gradient descent of glioma image

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IET IMAGE PROCESSING 2023年第14期17卷 3976-3985页

作者： Wang, Hongfei Peng, Xinhao Ma, ShiQing Wang, Shuai Xu, Chuan Yang, Ping Chinese Acad Sci Key Lab Adapt Opt Chengdu Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China Univ Elect Sci & Technol China Sch Med Chengdu Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Sichuan Acad Med Sci Dept Oncol Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Canc Inst Sichuan Acad Med Sci Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Dept Lab Med Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Sichuan Prov Key Lab Human Dis Gene Study Chengdu Sichuan Peoples R China

Brain tumour diagnosis is significant for both physicians and patients, but the low contrast and the artefacts of MRI glioma images always affect the diagnostic accuracy. The existing mainstream image enhancement methods are insufficient in improving contrast and suppressing artefacts simultaneously. To enrich the field of glioma image enhancement, this research proposed a glioma image enhancement method based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm. Firstly, this method modifies the cumulative distribution function on the image histogram and performs gamma correction on the image according to the modified histogram to obtain a contrast-enhanced image. Then, the method suppresses the artefacts of glioma images by total variational and wavelet denoising algorithm. To get better enhancement images, the optimal parameters in the proposed method are searched by the SPGD algorithm. The statistical studies performed on 580 real glioma images demonstrate that the authors' approach can outperform the existing mainstream image enhancement methods. The results show that the proposed method increases the discrete entropy of the image by 8.9% and the contrast by 2.8% compared to original images. The enhanced images are produced by the proposed method with a natural appearance, appealing contrast, less degradation, and reasonable detail preservation. To enrich the field of glioma image enhancement, this paper proposed a glioma image enhancement framework based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm. The results show that the proposed framework has superior performance compared to several existing glioma image enhancement methods. image

关键词： image denoising image enhancement parallel algorithms artefacts suppression contrast improvement histogram modification stochastic parallel gradient descent

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Bandwidth analysis and improvement of the beam phasing of fiber amplifiers using the stochastic parallel gradient descent algorithm

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OPTICS AND LASER TECHNOLOGY 2010年第7期42卷 1059-1065页

作者： Zhou, Pu Liu, Zejin Ma, Yanxing Wang, Xiaolin Ma, Haotong Xu, Xiaojun Natl Univ Def Technol Coll Optoelect Sci & Engn Changsha 410073 Hunan Peoples R China

We present the numerical analysis and improvement of the bandwidth of the beam phasing of fiber amplifiers using the stochastic parallel gradient descent (SPGD) algorithm. Time-varying phase distortions are incorporated into the dynamic simulation. It is shown by numerical calculation that the bandwidth of the standard SPGD algorithm is dependent on the phase distortions and on the number of lasers. The time-averaged cost function will decrease with an increase in the amplitude and frequency of phase distortions, and in the number of lasers. A cross-grouped SPGD algorithm is proposed, which offers attractive performance due to its robustness to various time-varying phase distortions and faster convergence rate for beam phasing of larger number of lasers. Dynamic simulation shows that the control bandwidth can be improved using the cross-grouped SPGD algorithm. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Coherent beam phasing Fiber amplifier stochastic parallel gradient descent

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Multimode fiber laser beam cleanup based on stochastic parallel gradient descent algorithm

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OPTICS COMMUNICATIONS 2011年第2期284卷 613-615页

作者： Zhao, Hai-Chuan Ma, Hao-Tong Zhou, Pu Wang, Xiao-Lin Ma, Yan-Xing Li, Xiao Xu, Xiao-Jun Zhao, Yi-Jun Natl Univ Def Technol Coll Optoelect Sci & Engn Changsha 410073 Hunan Peoples R China

We present experimental research on multimode fiber laser beam cleanup based on a stochastic parallel gradient descent (SPGD) algorithm. The multimode laser is obtained by injecting a 1064 nm central wavelength single mode fiber laser into a multimode fiber and the system is setup by using phase only liquid crystal spatial light modulators (LC-SLM). The quality evaluation function is increased by a factor of 10.5 and 65% of the laser energy is encircled in the central lobe when the system evolves from open-loop into close-loop state. Experimental results indicate the feasibility of the multimode fiber laser beam cleanup by adaptive optics (AO). (C) 2010 Elsevier B.V. All rights reserved.

关键词： Multimode fiber laser Spatial light modulators Beam cleanup stochastic parallel gradient descent

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Numerical and experimental study on liquid crystal optical phased array beam steering combined with stochastic parallel gradient descent algorithm

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OPTIK 2016年第3期127卷 1450-1454页

作者： Shi Yu-Bin Si Lei Feng Guo-Bin Zhang Jian-Min Northwest Inst Nucl Technol State Key Lab Laser Interact Matter Xian 710024 Peoples R China Natl Univ Def Technol Coll Optoelet Sci & Engn Changsha 410073 Hunan Peoples R China

In this paper, we present numerical and experimental study on beam steering with liquid-crystal optical phased array (LC-OPA) by means of stochastic parallel gradient descent (SPGD) algorithm. The numerical simulation, which is mainly based on the evolution of power-in-bucket (PIB), indicates that the beam steering strategy with LC-OPA is relatively robust, and the algorithm performs well most of the time. The results also reveal that the performance of SPGD algorithm is influenced by the order of Zernike polynomials, the radius of bucket and the steering angle. The order of Zernike polynomials and the radius of bucket are optimized to improve the accuracy and convergence of the algorithm. In a proof-of-concept experiment, the feasibility of the beam steering strategy with LC-OPA is demonstrated. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： Liquid crystal optical phased array stochastic parallel gradient descent Beam steering

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Convolutional Neural Network Combined With stochastic parallel gradient descent to Decompose Fiber Modes Based on Far-Field Measurements

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第18期41卷 5973-5982页

作者： Kim, Byungho Na, Jeongkyun Jeong, Yoonchan Seoul Natl Univ Dept Elect & Comp Engn Seoul 06288 South Korea Seoul Natl Univ ISRC Seoul 08826 South Korea Seoul Natl Univ IAP Seoul 08826 South Korea

Modal decomposition (MD) of fiber modes based on direct far-field measurement combining the convolutional neural network (CNN) with a stochastic parallel gradient descent (SPGD) algorithm is investigated both numerically and experimentally. For obtaining the modal coefficients of fiber modes guided in a large-mode-area fiber, the fiber modes are decomposed into a finite number of Hermite gaussian modes, the initial conditions of the modal coefficients are obtained through the CNN, and further optimization of them are carried out through the SPGD. The ambiguity problem that may happen in the CNN owing to the existence of the pair-beam field is resolved by properly labelling the phase differences with a single-valued parameter set in consideration of the mode-order indices. The feasibility and effectiveness of the proposed MD method is verified both numerical simulations and experimental demonstrations with both recorded image data and online real-time image data. The correlation error incurred by the proposed method is below 6.6 x 10(-4) and 8.7 x 10(-3) in the numerical simulations and the experimental demonstrations, respectively. The online real-time operation of the proposed method is also experimentally demonstrated at a decomposing rate of similar to 2 Hz.

关键词： Optical fiber networks Real-time systems Optical fiber devices Convolutional neural networks Charge coupled devices Optical variables measurement Optical fiber polarization Convolutional neural network deep learning modal decomposition multimode fiber stochastic parallel gradient descent

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Coherent beam combining of two fiber amplifiers using stochastic parallel gradient descent algorithm

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OPTICS AND LASER TECHNOLOGY 2009年第7期41卷 853-856页

作者： Zhou, Pu Liu, Zejin Wang, Xiaolin Ma, Yanxing Ma, Haotong Xu, Xiaojun Natl Univ Def Technol Coll Optielect Sci & Engn Changsha 410073 Hunan Peoples R China

We demonstrate the coherent beam combining of fiber amplifiers using a stochastic parallel gradient descent (SPGD) algorithm, producing a total output power of 23W. Active phase control on each amplifier is performed by running the SPGD algorithm on a DSP chip with updating rate of almost 100 kHz. Experimental results show that power encircled in the target pinhole in closed-loop mode is 1.85 times more than that in open-loop mode, which is 92% of the ideal case. The fringe contrast of the far-field fringe pattern is as high as 92% when the system is in closed-loop mode. The whole system in closed-loop mode performs well in a long-time observation. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Coherent beam combining Fiber amplifier stochastic parallel gradient descent

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Outdoor target-in-the-loop coherent beam combination using a stochastic parallel gradient descent algorithm 18

Outdoor target-in-the-loop coherent beam combination using a...

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Conference on Technologies for Optical Countermeasures XVIII and High-Power Lasers - Technology and Systems, Platforms, Effects V

作者： Bjorck, Matts Henriksson, Markus Sjokvist, Lars FOI Swedish Def Res Agcy Box 1165 S-58111 Linkoping Sweden

ISBN: (数字)9781510645790

ISBN: (纸本)9781510645790;9781510645783

This work presents a coherent beam combination (CBC) setup that consists of a four square tiled-aperture system with phase and tip-tilt control for each sub-beam. The system works at a wavelength of 1550 nm. Phase locking was performed with the stochastic parallel gradient descent (SPGD) algorithm, using the power reflected by a remote retro reflecting corner cube to a feedback detector located at the laser head. Experiments were performed for targets located at distances of 470 and 870 m during calm and partly cloudy conditions. The SPGD beam combining results are discussed and compared to optical wave propagation simulations. The optical wave propagation models that have previously been validated in a laboratory setting were extended to include atmospheric turbulence. In order to reproduce the experimental results it was important to include the propagation of the feedback signal from the retroreflector to the detector.

关键词： coherent beam combination tiled aperture stochastic parallel gradient descent

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