Abstract
Random number generation is the kernel of Monte Carlo method and simulation, and it's sometimes necessary to generate a random vector from an unknown distribution described by a group of weighted samples. Based on the idea of partial approximation, a novel Weighted-Sample-Based Random Vector Generation (WSB-RVG) algorithm is proposed in this paper, which skips the estimation of the unknown density and requires few assumptions on the concealed distribution. Thus this method is particularly suitable for random vector generation, and can be used for resampling in Particle Filter (PF) when the general Gaussian assumption deteriorates. Its validity and performances are verified in the simulations, where the proposed algorithm is compared with regularization, for approximating a Gaussian mixture model and resampling in a non-linear tracking.
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