A defensive marginal particle filtering method for data assimilation
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The Particle filtering (PF) method is often used to estimate the states of dynamical systems in a Bayesian framework. A major limitation of the standard PF method is that the dimensionality of the state space increases as the time proceeds and eventually may cause degeneracy of the algorithm. A possible approach to alleviate the degeneracy issue is to compute the marginal posterior distribution at each time step, which leads to the so-called marginal PF method. In this work we propose a defensive marginal PF algorithm which constructs a sampling distribution in the marginal space by combining the standard PF and the Ensemble Kalman filtering (EnKF) methods. With numerical examples we demonstrate that the proposed method has competitive performance against many existing algorithms.
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