Abstract: Assessing the importance of every particle is the key factor to ensure the accuracy of the dim target tracking based on particle filter. A small dim target tracking algorithm based on particle sparse discriminative representation is proposed in this paper to cope with the issue of the uncertainty of moving target tracking. An adaptive discriminative over-complete dictionary is trained and constructed according to infrared image, wherein the target dictionary describes the target signals character and the background dictionary embeds the background clutter. It is helpful to highlight the difference between the target particle and the background particle in the adaptive discriminative over-complete dictionary. The importance of every particle is constructed based on the significant residual difference between target particle and background particle, and then the observation model of particle filter is estimated to track target state. Meanwhile, the subspace of the over-complete dictionary is updated online by the stochastic estimation algorithm. Some experiments were carried out and the experimental results show this proposed approach could not only enhance the target state estimation ability of particle, but also improve the adaptive ability and target recognition accuracy of small dim moving target.