@inproceedings{7b9cb4abca2f432fa764063775fb27d2,
title = "Bayesian method for states estimation in structural health monitoring application",
abstract = "An important goal in structural health monitoring (SHM) is to identify the state of the structure in order to detect when damage occurs. In this paper, we tackle the state estimation problem in SHM systems. Therefore, a Bayesian approach based on particle filtering is proposed to perform the optimal online estimation. The proposed scheme relies on the introduction of an efficient importance density, based on the iterated square root central difference Kalman filter (ISRCDKF), which takes into consideration the current observation. The performance of this method is studied considering a complex three degree of freedom spring-mass-dashpot system. Simulation results show the efficiency of the suggested approach in terms of Root Mean Square Error (RMSE).",
keywords = "Iterated square root central difference Kalman filter, Kalman Filter, Particle filter, State Estimation, Structural health monitoring",
author = "Marwa Chaabane and Majdi Mansouri and Nouha Jaoua and {Ben Hamida}, Ahmed and Hazem Nounou",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2nd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2016 ; Conference date: 21-03-2016 Through 24-03-2016",
year = "2016",
month = jul,
day = "26",
doi = "10.1109/ATSIP.2016.7523119",
language = "English",
series = "2nd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "472--477",
booktitle = "2nd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2016",
address = "United States",
}