基于深度学习的分孔径相干合成系统相位控制方法

相干合成技术是突破单路激光亮度提升限制的重要技术途径,已成为激光技术领域的研究前沿和热点。按照孔径填充方法的不同,相干合成可以分为共孔径和分孔径合成两类。分孔径相干合成通过相位控制实现各子光束之间的波前匹配,有效地增大了发射口径、压缩了远场发散角,实现了亮度的提升。

在分孔径相干合成系统中,各路激光的动态相位噪声严重影响合成系统的效率、能量集中度和亮度,是限制合成系统向高功率、大阵元数目拓展的关键因素之一。为了克服动态相位噪声的影响,国内外科研人员相继提出了外差法、多频抖动法、单频抖动法、随机并行梯度下降算法等多种相位控制方法,并有效应用到不同类型的相干合成系统中。然而,上述相位控制方法存在一个共性难题:随着合成路数的拓展和相位噪声的增强,系统控制带宽会出现不同程度的下降,进而导致锁相残差增加、合成效率下降。

国防科技大学前沿交叉学科学院课题组首次将深度学习应用于分孔径相干合成系统,为解决上述难题提供了新的参考思路:构建一个卷积神经网络,在经过预先训练后,该网络便可以准确反映相位误差与可测量的合成光束光强分布之间的关系,进而直接补偿相位噪声。传统相位控制方法通常从合成光束的远场光强信息中提取相位控制信号;然而,深度学习的引入带来了新的技术挑战——合成光束的远场光强分布与阵列单元的相对相位之间并没有一一对应的关系,可能会因数据冲突导致网络失效。为了克服这个困难,该课题组提出在非焦平面上训练、以有效提取相位信息的方法,结果表明,在非焦平面处训练的卷积神经网络可以更准确地反映发射面光束阵列的相位分布,进而实现了动态相位噪声的高效精确补偿。

在此基础上,课题组通过7单元和19单元正六边形阵列相干合成系统对基于深度学习的相位控制方法进行了测试。利用合成光束的关键指标(包括斯特列尔比、条纹对比度和桶中功率)评估相位控制性能,验证了基于深度学习的相位控制方法的可行性和可拓展性。随着阵列单元数量的拓展,这种方法不会导致卷积神经网络的计算耗时和系统的复杂性增加,并且与经典的优化算法和抖动技术兼容。该研究结果发表在High Power Laser Science and Engineering 2019年第7卷第4期上(Tianyue Hou, Yi An, Qi Chang, Pengfei Ma, Jun Li, Dong Zhi, Liangjin Huang, Rongtao Su, Jian Wu, Yanxing Ma, Pu Zhou. Deep-learning-based phase control method for tiled aperture coherent beam combining systems[J]. High Power Laser Science and Engineering, 2019, 7(4): e59)。

该课题组周朴研究员表示:“我们将深度学习引入分孔径相干合成系统,验证了该方法的有效性和可拓展性,为解决大功率、大阵元相干合成系统的复杂控制难题提供了新的思路。”

这项工作中提出的方法为相干合成系统中相位控制带宽随阵列单元数量拓展而降低这一难题提供了新的解决方案。后续将进一步瞄准样本数据采集、网络结构设计和多维光场信息挖掘等方面对该控制方法进行综合优化,力争将该方法应用到大功率、超大阵元相干合成系统中。

实施基于深度学习相位控制方法的相干合成系统示意图

Deep-learning-based phase control method for tiled aperture coherent beam combining systems

Coherent beam combining (CBC) technology is an important technical approach to break through the brightness limitation of a single laser beam, and has become a frontier and hotspot of laser technology research. According to the aperture filling method, CBC can be classified into two categories: filled aperture combining and tiled aperture combining. Tiled aperture CBC achieves wavefront matching among beamlets through phase control, thus efficiently increases the emission aperture, compresses the far-field divergence angle, and improves the brightness.

In tiled aperture CBC system, the dynamic phase noise of each laser beam seriously affects the efficiency, energy concentration, and brightness of the combining system, and it is one of the key factors that limits the development of the combining system to high power and large array elements. In order to overcome the impact of dynamic phase noise, researchers have successively proposed various phase control methods, such as heterodyne detection, multi-dithering technique, single-frequency dithering technique, and stochastic parallel gradient descent algorithm, which have been effectively implemented to different types of CBC system. However, the above-mentioned phase control methods have a common problem: with the expansion of the combining channels and the enhancement of phase noise, the system control bandwidth would decrease to some degree, which leads to an increase in phase-locked residuals and a decrease in combining efficiency.

A research group from College of Advanced Interdisciplinary Studies, National University of Defense Technology has incorporated deep learning (DL) into tiled-aperture CBC systems for the first time, providing a new reference on solving the above-mentioned problem. The authors have shown that constructing a convolutional neural network (CNN), which can accurately reflect the relationship between the phase error and the measurable intensity distribution of the combined beam after pre-training, and then the phase noise could be directly compensated. Conventional phase control methods usually extract the phase control signals from the far-field intensity information of the combined beam. However, the introduction of DL brings a new technical challenge-there is no one-to-one correspondence between the far-field intensity distribution of the combined beam and the relative phases of the array elements, which may cause network failure due to data confliction. To overcome this challenge, the authors propose a method for training the CNN at the non-focal-plane to effectively extract phase information. The results show that the CNN trained at the non-focal-plane could reflect the phase distribution of the beam array at the source plane more accurately, and moreover, efficient and accurate compensation of dynamic phase noise could be achieved.

Furthermore, the DL-based phase control method has been tested by the CBC systems with 7-element and 19-element hexagonal arrays. Using key metrics (including Strehl ratio, fringe contrast and power in the bucket) of the combined beams to evaluate the phase control performance, the researchers have demonstrated the feasibility and extensibility of the DL-based phase control method. As the number of array elements increases, such a direct phase control method would not cause the increases in the computing time of CNN and the complexity of the CBC system, and it is compatible with classical optimization algorithms and dithering techniques. The research results are published in High Power Laser Science and Engineering, Vol. 7, Issue 4, 2019 (Tianyue Hou, Yi An, Qi Chang, Pengfei Ma, Jun Li, Dong Zhi, Liangjin Huang, Rongtao Su, Jian Wu, Yanxing Ma, Pu Zhou. Deep-learning-based phase control method for tiled aperture coherent beam combining systems[J]. High Power Laser Science and Engineering, 2019, 7(4): e59).

"We have incorporated DL into tiled aperture CBC systems and verified the effectiveness and extensibility of the method, which provides a new idea for solving the complex control problem of high-power, large-array CBC systems. " said Professor Pu Zhou from the research grup.

The method proposed in this work provides a new solution to the problem that in the CBC system, the phase control bandwidth decreases as the number of array elements increases. Follow-up will further aim at sample data collection, network structure design and multi-dimensional light field information mining to comprehensively optimize the control method, and strive to implement this method to high-power, ultra-large array CBC systems.

Schematic of the deep-learning-based phase control method implemented coherent beam combining system.