量子电子学报2025，Vol.42Issue(5)：602-610,9.DOI:10.3969/j.issn.1007-5461.2025.05.002
太赫兹光谱水蒸汽吸收噪声去除方法研究
Research on method of removing water vapor noise in terahertz spectroscopy
摘要
Abstract
A signal recovery method based on BP neural networks is proposed in this work to address the noise caused by water vapor absorption of terahertz waves in the environment.The method involves feature extraction on the amplitude of the collected signals in the frequency domain using principal component analysis(PCA),followed by the optimization of the back propagation(BP)neural network parameters using particle swarm optimization(PSO)algorithm.The results show that the BP neural network achieves the best signal recovery for internally validated explosives DMDP and RDX,with signal similarities of 0.995 and 0.999,respectively,the PCA combined with BP neural network(PCA-BP)achieves the signal similarity of 0.986 and 0.944,respectively,and the PSO-optimized PCA-BP(PSO-PCA-BP)recovery achieves the similarities of 0.987 and 0.997,respectively.For externally validated explosives 345TNT and NQ,the BP neural network's recovery similarity scores are 0.273 and 0.278,respectively,PCA-BP shows improvements with scores of 0.944 and 0.985,respectively,and PSO-PCA-BP yields the best recovery results with similarities of 0.946 and 0.993.Additionally,by combining phase and amplitude information,the time-domain signals are obtained using the inverse Fourier transform,which proves that the proposed method is equally effective in improving time-domain data.关键词
太赫兹光谱/水蒸汽噪声去除/BP神经网络/主成分分析/粒子群优化Key words
terahertz spectroscopy/water vapor noise removal/BP neural network/principal component analysis/particle swarm optimization分类
数理科学引用本文复制引用
寇冬阳,刘泉澄,邓琥,段勇威,尚丽平..太赫兹光谱水蒸汽吸收噪声去除方法研究[J].量子电子学报,2025,42(5):602-610,9.基金项目
国家自然科学基金(22305198) （22305198）
