Data Assimilation in Structural Dynamics: Extended-, Ensemble Kalman and Particle Filters
نویسنده
چکیده
Combined state and parameter estimation of dynamical systems plays a crucial role in extracting system response from noisy measurements. A wide variety of methods have been developed to deal with the joint state-parameter estimation of nonlinear dynamical systems. The Extended Kalman Filter method is a popular approach for the joint systemparameter estimation of nonlinear systems. This method combines the traditional Kalman filtering techniques with the linearisation tools to tackle nonlinear problems and its formulation is based on the assumption that the probability density function of the state vector can be reasonably approximated to be Gaussian. Recent research has been focused on non-Gaussian models. Of particular interest is the Ensemble Kalman Filter and the Particle Filter. These methods are capable of handling various forms of nonlinearities as well as non-Gaussian noise models. This paper examines and contrasts the feasibility of joint state and parameter identification in non-linear dynamical systems using the Extended Kalman, Ensemble Kalman and Particle filters.
منابع مشابه
Sequential data assimilation for streamflow forecasting using a distributed hydrologic model: particle filtering and ensemble Kalman filtering
Accurate streamflow predictions are crucial for mitigating flood damage and addressing operational flood scenarios. In recent years, sequential data assimilation methods have drawn attention due to their potential to handle explicitly the various sources of uncertainty in hydrologic models. In this study, we implement two ensemble-based sequential data assimilation methods for streamflow foreca...
متن کاملParticle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters*
This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particle filter, but is different from it in that the standard weight-type correction in the particle filter is complemented by the Kalman-type correction with the associated c...
متن کاملEnhanced Predictions of Tides and Surges through Data Assimilation (TECHNICAL NOTE)
The regional waters in Singapore Strait are characterized by complex hydrodynamic phenomena as a result of the combined effect of three large water bodies viz. the South China Sea, the Andaman Sea, and the Java Sea. This leads to anomalies in water levels and generates residual currents. Numerical hydrodynamic models are generally used for predicting water levels in the ocean and seas. But thei...
متن کاملStochastic Methods for Sequential Data Assimilation in Strongly Nonlinear Systems
This paper considers several filtering methods of stochastic nature, based on Monte Carlo drawing, for the sequential data assimilation in nonlinear models. They include some known methods such as the particle filter and the ensemble Kalman filter and some others introduced by the author: the second-order ensemble Kalman filter and the singular extended interpolated filter. The aim is to study ...
متن کامل4.8 Aspects of the Extended and Ensemble Kalman Filters for Land Data Assimilation in the Nasa Seasonal-to-interannual Prediction Project
Successful climate prediction at seasonal-to-interannual time scales may depend on the optimal initialization of the land surface states, in particular soil moisture (Koster and Suarez 2001). Such optimal initialization can be achieved by assimilating soil moisture observations into the land model prior to the forecast. We assess the performance of the Extended Kalman filter (EKF) and the Ensem...
متن کامل