A sequential importance sampling filter with a new proposal distribution for state and parameter estimation of nonlinear dynamical systems

Abstract

The problem of estimating parameters of nonlinear dynamical systems based on incomplete noisy measurements is considered within the framework of Bayesian filtering using Monte Carlo simulations. The measurement noise and unmodelled dynamics are represented through additive and/or multiplicative Gaussian white noise processes. Truncated Ito–Taylor expansions… (More)

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