To resolve weight degeneracy and avoid sample impoverishment in resampling algorithms of particle filter，a
method, named layered transacting MCMC-resampling algorithm, is proposed. When the effective sample size is below a
fixed threshold, particles are dived into two sample subsets according to their individual weights. Mutation operator and
PSO, which are considered as transition kernels of MCMC, are applied to sample subsets respectively. Then an acceptancerejection
rule of Metropolis-Hastings algorithm is used to generate the Markov chain with the stationary distribution which
is equivalent to target posterior density. The simulation results show that the proposed method is superior to other resampling
algorithms both in accuracy and convergence speed.