Research on the Temperature Variation Field Reconstruction Between the Main Reflector and Subreflector of the Large-Aperture Radio Telescope

ZHANG Xuan; LI Xijie; GAO Ming; LIN Shangmin; WANG Hu; LAI Yunqiang; LV Hong

doi:10.3788/gzxb20265507.0712003

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Research on the Temperature Variation Field Reconstruction Between the Main Reflector and Subreflector of the Large-Aperture Radio Telescope

更新时间：2026-06-10

- Research on the Temperature Variation Field Reconstruction Between the Main Reflector and Subreflector of the Large-Aperture Radio Telescope
- Acta Photonica Sinica Pages: 1-13(2026)
- 作者机构：
  
  1.西安工业大学光电工程学院西安 710021
  2.中国科学院西安光学精密机械研究所西安 710119
  3.中国科学院大学，北京， 100049
  4.西安市空间敏感器光学技术工程研究中心，西安，710119
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2021YFC2203501);National Natural Science Foundation of China(11803075)
- DOI：10.3788/gzxb20265507.0712003
  CLC： P404
- CSTR：32255.14.gzxb20265507.0712003
- Received：04 March 2026，
  
  Revised：2026-03-26，
  
  Accepted：30 March 2026，
- 稿件说明：
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张璇，李西杰，高明，等. 大口径射电望远镜主副面间温度变化场重建研究［J］.光子学报，2026，55（7）：0712003

ZHANG Xuan， LI Xijie， GAO Ming， et al. Research on the Temperature Variation Field Reconstruction Between the Main Reflector and Subreflector of the Large-Aperture Radio Telescope［J］. Acta Photonica Sinica， 2026， 55（7）：0712003
张璇，李西杰，高明，等. 大口径射电望远镜主副面间温度变化场重建研究［J］.光子学报，2026，55（7）：0712003 DOI： 10.3788/gzxb20265507.0712003. CSTR： 32255.14.gzxb20265507.0712003.

ZHANG Xuan， LI Xijie， GAO Ming， et al. Research on the Temperature Variation Field Reconstruction Between the Main Reflector and Subreflector of the Large-Aperture Radio Telescope［J］. Acta Photonica Sinica， 2026， 55（7）：0712003 DOI： 10.3788/gzxb20265507.0712003. CSTR： 32255.14.gzxb20265507.0712003.

摘要

针对大口径射电望远镜主副反射面间复杂环境导致的激光测量副面位姿精度下降的问题，提出了一种高精度的空间温度变化场重建方法。针对传统空间插值方法在处理大口径天线主副面间不均匀分层造成的垂直梯度突变温度场时的局限性，引入各向异性校正矩阵与位置相关的距离张量，创新性地将温度场分解为慢变背景项与瞬时脉动项，通过改进的反距离加权法、径向基函数、泛克里金法以及压缩感知稀疏重建的方法直接重建温度时空变化场。通过搭建南山25m射电望远镜空间立体环境监测模型进行外场实验，结果表明，改进的各向异性反距离加权方法在温度变化场重建中误差最小，更适用于大口径射电望远镜环境场的重建，也为后续风场参数反演与激光大气传输偏折精准校正奠定了核心理论与数据基础。

Abstract

The pose measurement accuracy of the main reflector and subreflector in the large-aperture radio telescope is severely degraded by the complex environmental disturbances. Specifically， non-uniform thermal stratification caused by solar radiation， heat concentration， and wind fields induces significant refractive index gradients. This leads to laser beam deflection， which undermines measurement stability. Accurate reconstruction of the three-dimensional temperature field is essential for evaluating and correcting such deflections. Conventional spatial interpolation methods struggle with the abrupt vertical temperature gradients and anisotropic structures inherent in this environment. Therefore， this study aims to develop a high-precision method for reconstructing the spatial temperature variation field. By integrating anisotropic corrections with a temporal decomposition strategy， this research overcomes the limitations of conventional methods in handling real-time dynamic characteristics in complex spatial correlations.To address the limitations of traditional spatial interpolation methods in handling abrupt vertical gradients caused by non-uniform stratification， a novel spatial temperature variation field reconstruction method is proposed. This method introduces an anisotropic correction matrix and a position-dependent distance tensor to adapt weight functions to local temperature gradient directions. The temperature field is decomposed into a slowly varying background term and an instantaneous fluctuation term. The slow-varying background term is obtained using a sliding time window sequence of discrete sensors， while the fluctuation term is reconstructed using improved anisotropic spatial interpolation. Based on this， traditional algorithms， including Inverse Distance Weighting （IDW）， Radial Basis Function （RBF）， Universal Kriging （UK）， and Compressive Sensing with Low-Rank Sparse （CS-LRS） reconstruction， are improved and evaluated. To validate the methodology， a three-dimensional non-uniform temperature stratification simulation environment was established with superimposed Gaussian dynamic disturbances. Furthermore， a spatial three-dimensional environmental monitoring model was constructed on the Nanshan 25m radio telescope， utilizing an array of 30 high-precision temperature sensors for field experiments.In the simulation analysis， directly interpolating absolute temperatures resulted in obvious step smoothing distortions and localized bullseye effects. In contrast， the proposed temperature variation field reconstruction， by filtering out the slow-varying background， clearly restored the two disturbance air masses with well-preserved details. Quantitative results showed the average reconstruction error at six test points decreased from 0.998 °C with direct interpolation to 0.102 °C with the proposed model. The field experiment on the Nanshan 25 m radio telescope confirmed the presence of a significant vertical temperature gradient superimposed with high-frequency temperature pulsations. Using 24 sensors as sampling inputs and 6 as validation points， the reconstruction errors were evaluated via Mean Absolute Error （MAE） and Root Mean Squared Error （RMSE）. The experimental results demonstrated that the four improved methods in the proposed model exhibited significantly higher accuracy compared to traditional direct reconstruction. Among them， the anisotropic IDW method achieved the highest reconstruction accuracy for both the temperature field and the temperature variation field， with an RMSE as low as 0.11 °C. Conversely， CS-LRS showed the worst precision in temperature field reconstruction， while anisotropic RBF performed the worst in the variation field reconstruction. Furthermore， the analysis of specific locations revealed that sensors in the intermediate free air layer， which are less affected by the extreme temperature differences of the reflectors， exhibited the smallest reconstruction errors.The proposed temperature variation field reconstruction method， which combines anisotropic interpolation with background-fluctuation separation， effectively overcomes the limitations of traditional spatial interpolation in complex environments. By reconstructing only the transient turbulent fluctuations， the model circumvents severe errors induced by steep background gradients. The anisotropic IDW algorithm demonstrates the best overall performance and is the most suitable for the environmental field reconstruction of the large-aperture radio telescope. This work provides a core theoretical and data foundation for subsequent wind-field parameter inversion and real-time precise correction of laser atmospheric transmission deflection.

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references

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