  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.
