首页|基于自适应红外多波段联合光谱分析的高精度气体浓度反演研究

基于自适应红外多波段联合光谱分析的高精度气体浓度反演研究

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本文提出一种自适应多波段联合浓度反演算法,结合透过率稳定区间与谱宽阈值自适应选择待测气体的有效波段;采用非线性最小二乘拟合方法对各有效波段进行浓度反演及残差分析,获得各有效波段的浓度反演结果及其权重,通过加权平均实现待测气体浓度的精确定量分析.设计并进行实验验证,结果表明,自适应多波段联合浓度反演算法的稳定系数达到了 0.9976,与传统的单波段及多波段浓度反演算法相比,该反演结果的均方根误差分别降低了 64.44%和41.52%,平均相对误差分别降低了 65.97%和46.72%,平均绝对误差分别降低了 66.32%和47.74%,反演精度与稳定性得到了明显提升.
Research on high-precision gas concentration inversion based on adaptive infrared multi-band joint spectral analysis
In this paper,we proposed an adaptive multi-band joint concentration inversion algorithm,which combines the transmittance stable range and the spectral width threshold to adaptively select the effective band of the measured gas.The nonlinear least squares fitting method is used to invert the concentration of each effective band and analyze the residual to obtain the concentration inversion results and their weights of each effective band.The accurate quantitative analysis of the concentration of the measured gas is realized by weighted averaging.The algorithm verification experiment is carried out.The results show that the stability coefficient of the adaptive multi-band joint concentration inversion algorithm is 0.9976.Compared with the traditional single-band and multi-band concentration inversion algorithms,the root mean square error of the inversion results is reduced by 64.44%and 41.52%,the mean relative error is reduced by 65.97%and 46.72%,and the mean absolute error is reduced by 66.32%and 47.74%respectively.It can be concluded that the inversion accuracy and stability are significantly improved.

effective band selectionresidual analysisweight averageadaptive multi-band joint concentra-tion inversion

王冠程、赵百轩、郑凯丰、陈宇鹏、赵莹泽、秦余欣、王惟彪、刘国豪、盛开洋、吕金光、梁静秋

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中国科学院长春光学精密机械与物理研究所应用光学国家重点实验室,吉林长春 130033

中国科学院大学,北京 100049

有效波段选择 残差分析 加权平均 自适应多波段联合浓度反演

2024

中国光学
中国科学院长春光学 精密机械与物理研究所 中国光学学会

中国光学

CSTPCD北大核心
影响因子:2.02
ISSN:2095-1531
年,卷(期):2024.17(6)