This paper is investigating the mixture autoregressive model with constant mixing weights in state space form and generalization to ARMA mixture model. Using a sequential Monte Carlo method, the forecasting, filtering and smoothing distributions are approximated and parameters f the model is estimated via the EM algorithm. The results show the dimension of parameter vector in state space representation reduces. The results of the simulation study show that the proposed filtering algorithm has a steady state close to the real values of the state vector. Moreover, according to simulation results, the mean vectors of filtering and smoothing distribution converges to state vector quickly.