The invention provides an unscented particle filtering method based on a particle swarm optimization algorithm, which comprises the steps of: 1, initial time: obtaining a group of initial particles from an initial distribution p(x0), setting an initial average and variance of the group of initial particles; 2, sampling sequential importance; 3, updating weight; 4, obtaining an normalized weight; 5, sampling; 6, updating state; and 7, solving a globally optimum solution G(t) at the current time. The invention ensures that a particle swarm more trends to a high likelihood region before the weight is updated through a particle swarm optimization process, thereby solving the problem of sample depletion to a certain degree. The optimization process ensures particles far away from real state trends to an area with high occurrence probability of the real state, thereby improving the action effect of each particle. Compared with other intelligent optimization algorithms, the particle swarm optimization algorithm has the advantages of easy implementation and no adjustment on various parameters, lowers the particle number required by accurate estimation, and improves the computing efficiency of filtering.