In this correspondence, an improvement on resampling algorithm (also called the systematic resampling algorithm) of particle filters is presented. First, the resampling algorithm is analyzed from a new viewpoint and its defects are demonstrated. Then some exquisite work is introduced in order to overcome these defects such as comparing the weights of particles by stages and constructing the new particles based on quasi-Monte Carlo method, from which an exquisite resampling (ER) algorithm is derived. Compared to the resampling algorithm, the proposed algorithm can maintain the diversity of particles thus avoid the sample impoverishment in particle filters, and can obtain the same estimation accuracy through less number of sample particles. These advantages are finally verified by simulations of non-stationary growth model and a re-entry ballistic object tracking.