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Proceedings of the International Conference on
Advances and New Challenges in Earthquake
Engineering Research, Hong Kong Volume
555
UNSCENTED PARTICLE FILTER FOR TIME DOMAIN
IDENTIFICATION OF NONLINEAR STRUCTURAL DYNAMIC
SYSTEMS
K.Y. Koo and C.B. Yun
Department of Civil and Environmental Engineering,
Korea Advanced Institute of Science and Technology, Daejoen, Korea
ABSTRACTS
In this study, a recently developed unscented particle filter (UPF) technique is studied for
identification of nonlinear structural dynamic systems. It is well-known that there is no general
solution for parameter identification of nonlinear structural system. As an alternative in the sense
of linear approximation solution, the Extended Kalman filter (EKF) has been frequently employed
for identification of time-varying structural parameters. However, the EKF has several drawbacks
such as biased estimations and erroneous estimations especially for highly nonlinear dynamic
systems due to its crude linearization scheme. To overcome the weakness of the EKF, the UPF was
recently developed. The UPF is a novel method for nonlinear, non-Gaussian, and on-line
estimation. The algorithm is a particle filter that uses an unscented Kalman filter (UKF) to generate
the importance proposal distribution.
Numerical simulation studies have been carried out on SDOF and MDOF systems. The results on
the linear and nonlinear SDOF systems show that the UPF gives more accurate and robust estimates
under the existence of the system and measurement errors with rough initial guesses for the states
and the error covariance matrices. The results on a five-story building structure subjected to
nonlinear behavior at the bottoms of the columns show that the UPF has good estimation capability.
The results from a series of numerical simulations indicate that the UPF is superior to the EKF for
the system identification of nonlinear dynamic systems especially for highly nonlinear systems.
1. INTRODUCTION
Detection of structural change or damage is one of the most important and challenging issues in
the structural health monitoring system. Statistical inference approaches are more appropriate in
the detection of the structural change due to the complex nature of civil infrastructures and
noise-polluted measurements. Especially Bayesian approach in statistical inference has useful
structure in on-line estimation and has been applied to various fields, i.e., physics, control system
and signal processing.