For the nonlinear non-Gaussian filtering problem of observation data in sparseness sampling environment, a novel auxiliary Gaussian sum quadrature particle filter (AGSQPF) based on target characteristics is proposed. In the proposed algorithm, the predicted and the posterior probability density function of target state are approximated by finite Gaussian mixtures based on Gauss-Hermite quadrature and the particle filtering. Moreover, the proposed algorithm can incorporate target speed, time interval and the latest observation information into the importance density function, which can effectively improve the performance. The simulation results show that the performance of the proposed algorithm is much better than Gaussian sum quadrature particle filter (GSQPF) for sparseness sampling environment.