  A resampling scheme provides a way to switch low-weight particles for
sequential Monte Carlo with higher-weight particles representing the objective
distribution. The less the variance of the weight distribution is, the more
concentrated the effective particles are, and the quicker and more accurate it
is to approximate the hidden Markov model, especially for the nonlinear case.
We propose a repetitive deterministic domain with median ergodicity for
resampling and have achieved the lowest variances compared to the other
resampling methods. As the size of the deterministic domain $M\ll N$ (the size
of population), given a feasible size of particles, our algorithm is faster
than the state of the art, which is verified by theoretical deduction and
experiments of a hidden Markov model in both the linear and non-linear cases.
