The new method is demonstrated on the simulation of a crowd of people traversing a train station and is able to estimate both their current position (a continuous, Gaussian variable) and their chosen destination (a categorical parameter). Compared to similar techniques for mixed state estimation, the RJUKF has the advantage of being efficient enough for online (i.e. The proposed method is able to conduct data assimilation on both continuous and categorical parameters simultaneously. This paper presents a new method, the RJUKF, that combines the Unscented Kalman Filter (UKF) data assimilation algorithm with elements of the Reversible Jump (RJ) Markov chain Monte Carlo method. However, existing methods face difficulties working with categorical parameters, which are common in agent-based models. Commonly-used data assimilation methods are being adapted for use with agent-based models with the aim of allowing optimisation in response to new data in real-time.
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December 2022
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