Design of Efficient Point-Mass Filter with Application in Terrain Aided Navigation

Fuente: arXiv
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Autori principali: Matoušek, J., Duník, J., Brandner, M.
Natura: Preprint
Pubblicazione: 2023
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author Matoušek, J.
Duník, J.
Brandner, M.
author_facet Matoušek, J.
Duník, J.
Brandner, M.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2303_05100
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Design of Efficient Point-Mass Filter with Application in Terrain Aided Navigation
Matoušek, J.
Duník, J.
Brandner, M.
Systems and Control
Signal Processing
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.
title Design of Efficient Point-Mass Filter with Application in Terrain Aided Navigation
topic Systems and Control
Signal Processing
url https://arxiv.org/abs/2303.05100