Abstract
Coherent beam combining (CBC) with closely arranged centrosymmetric arrays is a promising way to obtain a high-brightness laser. An essential task in CBC is to actively control the piston phases of the input beams, maintaining the correct phasing to maximize the combination efficiency. By applying the neural network, the nonlinear mapping relationship between the far-field image and the piston phase could be established, so that the piston phase can be corrected quickly with one step, which caused widespread concern. However, there exists a piston-type phase ambiguity problem in the CBC system with centrosymmetric arrays, which means that multiple different piston phases may generate the same far-field image. This will prevent the far-field image from correctly reflecting the phase information, which will result in a performance degradation of the image-based intelligent algorithms. In this paper, we make a theoretical analysis of phase ambiguity. A method to solve phase ambiguity is proposed, which requires no additional optical devices. We designed simulations to verify our conclusions and methods. We believe that our work solves the phase ambiguity problem in theory and is conducive to improving the performance of image-based algorithms.
Highlights
Published: 17 January 2022High-power lasers are widely applied in many fields, including Lidar systems, SpaceCommunication, Laser Medicine, Material Processing, and so on [1,2,3,4,5,6]
We prove that piston-type phase ambiguity will occur in coherent beam
We prove with that piston-type phasedistribution ambiguity of will occur in coherent beam combining (CBC)
Summary
High-power lasers are widely applied in many fields, including Lidar systems, Space. Communication, Laser Medicine, Material Processing, and so on [1,2,3,4,5,6]. In 2020, Liu et al employed CNN to measure the beam-pointing and piston phase of sub-beams from the far-field image in a two-beam coherent beam combining system [28]. These studies indicate that the introduction of image information can effectively improve the convergence-performance of the algorithm. Due to such phenomena, predictions of NN are reliable only when all the causes of phase ambiguity have been solved. The introduced image information will accurately reflect the piston phase, which is helpful for a high-speed and accurate prediction
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