A signal separation and parameter extraction method based on particle filtering for single channel sinusoidal frequency modulated (SFM) signals is put forward. By assuming that the frequency of SFM signals mixture is continuous, a phase-difference de-aliasing arithmetic based on particle filtering is proposed. And the dimension of state space is reduced by using phase-difference between source signals. A likelihood function model suitable for high dimensional state space is proposed. Particles weight is accurately measured by comparing error between estimated values and true values of particles with fixed length. The problem of particle diversity reduction in the static parameters situation is solved by the introduction of Markov-chain Monte Carlo (MCMC) transfer after re-sampling, and the speed of particle filter iteration convergence is also effectively improved. Single channel SFM signal parameters are extracted and signals are separated by reconstructing signals only with the prior knowledge of modulation type. Finally, the simulation results indicate that this method can separate the multi-component signal sources and estimate the parameters effectively.