Accurate Density-Weighted Convolution for Point-Mass Filter and Predictor

Duník, Jindřich; Straka, Ondřej; Matoušek, Jakub; Brandner, Marek

doi:10.1109/taes.2021.3079568

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E On the Compatibility With State Of The Art Algorithms1

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2022

2023

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Cited by 4 publications

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“…Because considered models have linear dynamics, and only one row of the TPM has to be calculated, the ePMF is ideal to be combined with the reliable convolution algorithm, to achieve higher accuracy of estimates [29], [30].…”

Section: E On the Compatibility With State Of The Art Algorithmsmentioning

confidence: 99%

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

Matoušek¹,

Duník²,

Brandner³

2023

Preprint

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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-pointbased 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.

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Section: E On the Compatibility With State Of The Art Algorithmsmentioning

confidence: 99%