An improved method is proposed to deal with the low estimation accuracy and convergence rate in fixed parameters estimation of mixed digital signals using particle filtering. By modification of the traditional random walk model, and modeling the posterior probability of the parameters as the BETA distribution, the estimation accuracy and convergence rate are enhanced, the separation performance is improved either. In order to evaluate the performance of the proposed algorithm, the joint Cramer-Rao bound for parameter estimation is derived under the condition of known transmitted symbols. Simulation results show that the new algorithm has better performance than that of the traditional methods.