To deal with non-linear, non-Gaussian state estimation problem, a kind of particle filter algorithm based on interval estimation was proposed. This paper analyzed the auxiliary particle filter at first. After interval estimating the expectation of the system states, the new algorithm sampled uniformly in the interval and updated the filter results using the new measurement. The interval extension conditions were proposed to ensure the validity of the estimated range and computational efficiency of the algorithm. Sampling uniformly in the interval avoids the particle degeneracy and improves the particle divergence. The experimental results show that the new particle filter is significantly better than the general particle filter. 