Design of Efficient Point-Mass Filter with Application in Terrain Aided Navigation
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 efficient PMF (ePMF) estimator, unifying continuous and discrete, approaches is proposed, designed, and discussed. By numerical illustrations, it is shown, that the proposed ePMF can lead to a time complexity reduction that exceeds 99.9% without compromising accuracy. The MATLAB code of the ePMF is released with this paper.
Cite
@article{arxiv.2303.05100,
title = {Design of Efficient Point-Mass Filter with Application in Terrain Aided Navigation},
author = {J. Matoušek and J. Duník and M. Brandner},
journal= {arXiv preprint arXiv:2303.05100},
year = {2024}
}
Comments
Pulibshed in proccedings of FUSION 2023. PLEASE cite the published version! The code is also now available at GitHub: https://github.com/IDM-UWB/efficient-PMF