The invention provides a target tracking method based on Markov chains Monte-Carlo particle filtering, which comprises steps of: 1, obtaining a group of initial particles from initial distribution and setting the initial mean value and variance of the initial particles at initial time; 2, sampling importance; 3, updating a weight number; 4, obtaining a normalized weight number; 5, resampling; 6, introducing an MCMC (Markov Chains Monte-Carlo) movement step; and 7, updating status. By the MCMC movement step, the invention pushes particles to an area with larger prior distribution and posterior distribution, improves the diversity of the particles and inhibits the depletion problem of a sample to some extent. The solvent of the depletion problem of the sample ensures the effect of algorithm resample so as to further enhance the precision of filtering. The MCMC movement step is easy to realize, thereby being capable of combining with other improvement steps to optimize the particle filtering. The MCMC movement step is added to increase the workload of a filtering method and decreases number of particles needed in accurate estimation, thereby enhancing the filtering efficiency.