Parameter estimation of high-speed railway vehicle using improved Rao-Blackwellised Particle Filter

In this paper, The method of parameter estimation for railway vehicle is discussed. To support condition-based maintenance based on diagnosing the fault of vehicle, We build a CRH2 high-speed railway vehicle lateral state space model and use Rao-Blackwellised Particle Filter(RBPF)-based method for parameter estimation. However, the standard RBPF-based method does not adapt to non-Gaussian noise when verified using the real track irregularity as the input of model instead of Gaussian noise. An improved RBPF estimation method is introduced which can estimate parameters with real track irregularity.