In view of the difficult in the chaotic signal detection and track in the low signal-to-noise(SNR) environment, a modified SR-UKF-PF is developed that has much better robustness than the traditional SR-UKF and gets almost the same performance as the Particle filter. The main idea of this algorithm is to calculated by the system state transition matrix and the error covariance matrix which are gained from the SR-UKF and the sequential fusion to construct the importance density function of the particle filter. Then the importance density function can integrates the latest observation into system state transition density, and the proposal distribution can approximate the posterior distribution maximumly. To demonstrate the effectiveness of this model, simulations are carried out based on tracking algorithm for the typical chaotic time series of low dimension chaos mapping and super chaos mapping. The simulation results show that this algorithm can overcome the flaw that it is hard to get the optimization importance density function in the particle filter and significantly improves the accuracy of state estimation, and demonstrates the superiorities of particle filtering in the low SNR.