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arXiv · 2302.13827

Efficient Point Mass Predictor for Continuous and Discrete Models with Linear Dynamics

Jakub Matousek·Jindrich Dunik·Marek Brandner·Chan Gook Park·Yeongkwon Choe
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Abstract

This paper deals with state estimation of stochastic models with linear state dynamics, continuous or discrete in time. The emphasis is laid on a numerical solution to the state prediction by the time-update step of the grid-point-based point-mass filter (PMF), which is the most computationally demanding part of the PMF algorithm. A novel way of manipulating the grid, leading to the time-update in form of a convolution, is proposed. This reduces the PMF time complexity from quadratic to log-linear with respect to the number of grid points. Furthermore, the number of unique transition probability values is greatly reduced causing a significant reduction of the data storage needed. The proposed PMF prediction step is verified in a numerical study.

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BibTeXRIS

Jakub Matousek, Jindrich Dunik, Marek Brandner, Chan Gook Park, Yeongkwon Choe. 2023-02-24. Efficient Point Mass Predictor for Continuous and Discrete Models with Linear Dynamics. https://doi.org/10.1016/j.ifacol.2023.10.621

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