For the estimation accuracy problem that a particle filter used in hidden state tracking in nonlinear state mutation system suffers from particle impoverishment, a self-adaptive resampling particle filter based on student's t distribution is proposed.Firstly, the algorithm employs the student's t distribution as the transfer function of sampling scale. Then, the particle set is divided into two subsets according to the weight. Finally, self-adaptive crossover and mutation operations are performed on the two subsets to obtain the newborn sets. This algorithm can improve the estimation accuracy by self-adaptively improving the particle diversity. The simulation results show the feasibility and effectiveness of the proposed algorithm.