In sequential Monte Carlo methods, a resampling strategy is employed to replace low-weight particles with those of higher weight that better represent the target distribution. The goal is to reduce the variance among particle weights, which in turn concentrates the distribution of effective particles. This concentration facilitates a more rapid and precise approximation of the hidden Markov model, particularly for the nonlinear case. Typically the distribution of these particles is skewed, we introduce a method of repeated ergodicity within a deterministic domain, utilizing the median for resampling and this approach has resulted in the lowest variance when compared to alternative resampling techniques. With the deterministic domain size being much smaller than the population size, and under reasonable assumptions regarding particle size, our algorithm outperforms contemporary methods. This has been substantiated through theoretical analysis and empirical testing on hidden Markov models, encompassing both linear and nonlinear cases.