TY - GEN
T1 - Model-based Parameter Estimation Method for Terahertz Signals
AU - Xu, Yafei
AU - Zhang, Liuyang
AU - Chen, Xuefeng
AU - Zhang, Zhen
AU - Shen, Zhonglei
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/15
Y1 - 2020/10/15
N2 - Parameter estimation hasn't been investigated in terahertz non-destructive testing (NDT) field due to the lack of the prior distribution knowledge of terahertz signals. In this study, a statistical model of terahertz signal is proposed to model the terahertz echo signal. The critical issue mainly focuses on how to obtain the optimal parameter estimation from the complex terahertz echo signal. Therefore, we provide two estimators: the maximum likelihood estimation (MLE) based on expectation maximization algorithm (EM), and the lasso estimation based on the sparse representation. Simulation and experiments have been implemented to analyze the fitting performance of the proposed model. The results validate the effectiveness and applicability of the statistical model, and indicate that the Lasso estimator outperforms the EM estimator for the parameter estimation of terahertz signal, which provides a new statistical distribution model and parameter estimation method for the terahertz signal in terahertz NDT.
AB - Parameter estimation hasn't been investigated in terahertz non-destructive testing (NDT) field due to the lack of the prior distribution knowledge of terahertz signals. In this study, a statistical model of terahertz signal is proposed to model the terahertz echo signal. The critical issue mainly focuses on how to obtain the optimal parameter estimation from the complex terahertz echo signal. Therefore, we provide two estimators: the maximum likelihood estimation (MLE) based on expectation maximization algorithm (EM), and the lasso estimation based on the sparse representation. Simulation and experiments have been implemented to analyze the fitting performance of the proposed model. The results validate the effectiveness and applicability of the statistical model, and indicate that the Lasso estimator outperforms the EM estimator for the parameter estimation of terahertz signal, which provides a new statistical distribution model and parameter estimation method for the terahertz signal in terahertz NDT.
KW - EM
KW - parameter estimation
KW - prior statistical model
KW - sparse representation
KW - terahertz NDT
UR - https://www.scopus.com/pages/publications/85098561814
U2 - 10.1109/ICSMD50554.2020.9261749
DO - 10.1109/ICSMD50554.2020.9261749
M3 - 会议稿件
AN - SCOPUS:85098561814
T3 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings
SP - 378
EP - 383
BT - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020
Y2 - 15 October 2020 through 17 October 2020
ER -