Deep learning hybrid agent model for atmospheric environment parameter calibration in high-precision measurement devices

doi:10.3785/j.issn.1008-973X.2025.07.023

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (7): 1547-1556 DOI: 10.3785/j.issn.1008-973X.2025.07.023

Deep learning hybrid agent model for atmospheric environment parameter calibration in high-precision measurement devices

Xinbo YUAN1(

),Zhaobin XU1,2,*(

),Ziru LI1,Jian PAN1,Xiaojun JIN1,2,3,Zhonghe JIN1,2,3

1. Micro-Satellite Research Center, Zhejiang University, Hangzhou 310027, China
2. Key Laboratory of Micro-Nano Satellite Research of Zhejiang Province, Hangzhou 310027, China
3. Huanjiang Laboratory, Zhuji 311899, China

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Abstract

A high-precision physical field hybrid agent fitting model based on deep learning theory was proposed, in order to improve the compensation accuracy of a measurement device for the atmospheric refraction index in-field calibration. The sensor array was expanded by forward and backward time series prediction, the atmospheric refraction compensation system was optimized by obtaining high-accuracy environmental parameters, and the density distribution of sensor array observation points was increased at the same moment. Taking the temperature field among meteorological parameters as an example, simulation results show that the proposed model achieves higher accuracy than traditional multi-point compensation models. The proposed model improved the accuracy of environmental parameters by 71.8% and reduced the standard deviation by 73.1%. Monte Carlo simulation analysis demonstrates that the proposed model exhibits stronger stability compared to traditional multi-point methods.

Key words： measurement device environmental parameter sensor array time series analysis radial basis function interpolation

Received: 26 June 2024 Published: 25 July 2025

CLC:

TN 98

Fund: 国家自然科学基金资助项目（U21A20443，62073289）.

Corresponding Authors: Zhaobin XU E-mail: xinbo@zju.edu.cn;zjuxzb@zju.edu.cn

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Cite this article:

Xinbo YUAN,Zhaobin XU,Ziru LI,Jian PAN,Xiaojun JIN,Zhonghe JIN. Deep learning hybrid agent model for atmospheric environment parameter calibration in high-precision measurement devices. Journal of ZheJiang University (Engineering Science), 2025, 59(7): 1547-1556.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.07.023 OR https://www.zjujournals.com/eng/Y2025/V59/I7/1547

高精度测量装置大气环境参数校准深度学习混合代理模型

为了提高测量装置在野外校准时的大气折射率补偿精度，提出基于深度学习理论的高精度物理场混合代理拟合模型. 正逆向时间序列预测扩充传感器阵列，获取高精度的环境参数优化大气折射补偿系统，增加同一时刻传感器阵列观测点位的分布密集程度. 以气象参数中的温度场为例，仿真结果表明，所提模型相较于传统多点法补偿模型具有更高的精度，所提模型使环境参数的精度提高了71.8%，标准差降低了73.1%. 蒙特卡洛仿真分析的结果证明，所提模型相较于传统多点法具有更强的稳定性.

关键词： 测量装置, 环境参数, 传感器阵列, 时间序列分析, 径向基函数插值

Fig.1 Architecture diagram of typical 1D convolutional neural network

Fig.2 Performance comparison for activation functions

Fig.3 Model for expanding sensor array based on deep learning

Fig.4 Schematic diagram of reverse time series analysis

Fig.5 Model of expanding sensor array based on forward and backward time series

Fig.6 Runge’s phenomenon caused by high-order interpolation

Fig.7 Algorithm framework for combination of radial basis function interpolation and polynomial fitting

Fig.8 Simulation model for range measurement environment

Fig.9 Simulation comparision chart of expanding sensor array

Fig.10 Comparison of average temperature deviation before and after expanding sensor array

Tab.1 Logarithmic root mean square error for polynomial fittings of different degrees

Fig.11 Physical field for different interpolation methods

Fig.12 Loss function during polynomial fitting (root mean square error as loss function)

Fig.13 Fitting loss function with adaptive regularization

Fig.14 Regularization effects during polynomial fitting

Fig.15 Normal distribution analysis of Monte Carlo simulation

Tab.2 Mean and standard deviation of normal distribution

Fig.16 Schematic diagram of sensor compensation system

Fig.17 Monitoring results of temperature and humidity variations in measurement line

Fig.18 Indoor experimental platform

Tab.3 Range deviations before and after correction


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