Abstract: As the problem of particles degradation exists in the traditional particle filter algorithm,a target tracking algorithm based on the Markov chain Monte Carlo(MCMC) unscented particle filter is proposed.Instead of taking a transition prior probability as proposal distribution,the unscented Kalman filter(UKF) is used to generate the proposal distribution so as to improve the filtering effect.Then the paper syncretizes the standard MCMC sampling method,Metropolis Hastings(MH),and the unscented particle filter,which can reduce the effect that the traditional particle filter doesn't consider the current measurement.The syncretized algorithm takes the current measurement into the filtering process and makes the particles more diversification.Experiment results show that the algorithm has more significant advantages in tracking accuracy than other traditional algorithms.