Self Adaptive Particle Filter for Structural System Identification

Abstract eng:
A self adaptive particle filter method for structural system identification is presented in this paper. Such an adaptive technique that uses statistical methods to adapt the number of particles at each iteration. This method improves the efficiency of state estimation by adapting the size of sample sets during the estimation process through KLD-Sampling. Within this adaptation process the number of samples is increased if the state uncertainty is high and decreased if the density distribution is focused on a small part of the state space. Simulation results of system identification for tracking the dynamic parameter changes are presented to demonstrate the effectiveness of the proposed method.

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