Design of Efficient Point-Mass Filter with Application in Terrain Aided Navigation
arxiv(2023)
摘要
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
accuracy. The MATLAB code of the ePMF is released with this paper.
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