Efficient Point Mass Predictor for Continuous and Discrete Models with Linear Dynamics
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.
Cite
@article{arxiv.2302.13827,
title = {Efficient Point Mass Predictor for Continuous and Discrete Models with Linear Dynamics},
author = {Jakub Matousek and Jindrich Dunik and Marek Brandner and Chan Gook Park and Yeongkwon Choe},
journal= {arXiv preprint arXiv:2302.13827},
year = {2023}
}
Comments
Accepted for IFAC 2023