Particle filter (PF) algorithm has been applied in neural decoding, but rarely in movement trajectory reconstruction of hippocampal place cells. According to the response characteristics of place cells, the state space population encoding model of movement trajectory was established with quadratic exponential Poisson’s equation in this paper, the performance of the PF algorithm was investigated with respective simulated data and real data in movement trajectory reconstruction. And the results were then compared with extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithms. For the simulated data, the number of place cells needed by the PF is less than that of the others, under the same reconstruction precision. For the real data, correlation coefficient and root mean square error between true trajectories and reconstructed trajectories by the PF are superior to that of by the EKF algorithm and by the UKF algorithm. These results demonstrate that not only does the PF algorithm efficiently utilize encoding information of place cells population, but also has an outstanding movement trajectory reconstruction performance. It would provide powerful technique support for further research in spatial cognitive mechanism.