Particle filter (PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter (KF) and those improved KFs such as extended KF (EKF) and unscented KF (UKF).However,problems such as particle depletion and particle degradation affect the performance of PF.Optimizing the particle set to high likelihood region with intelligent optimization algorithm results in a more reasonable distribution of the sampling particles and more accurate state estimation.In this paper,a novel bird swarm algorithm based PF (BSAPF) is presented.Firstly,different behavior models are established by emulating the predation,flight,vigilance and follower behavior of the birds.Then,the observation information is introduced into the optimization process of the proposal distribution with the design of fitness function.In order to prevent particles from getting premature (being stuck into local optimum) and increase the diversity of particles,Lévy flight is designed to increase the randomness of particle’s movement.Finally,the proposed algorithm is applied to estimate the speed of the train under the condition that the measurement noise of the wheel sensor is non-Gaussian distribution.Simulation study and experimental results both show that BSAPF is more accurate and has more effective particle number as compared with PF and UKF,demonstrating the promising performance of the method.