Aiming at the problems in particle filter that the importance density function is difficult to select and the system state covariance matrix may appear negative definiteness, this paper puts forward a new kind of Singular Value Decomposition Unscented Particle Filter(SVDUPF)algorithm. The algorithm adopts adaptive factor to regulate the dynamic model error, uses the singular value decomposition to restrain the negative definiteness of the matrix, and uses the improved UKF algorithm to generate the importance density function, which can make up for the defects of the particle filter. The proposed algorithm is used in the single-variable, non-static state growth model for simulations. The results show that the proposed algorithm has a filtering precision obviously better than that of EKF and UPF algorithm, which can improve the calculating precision of the model.