For the problem of survive particle importance sampling in multitarget tracking Probability Hypothesis Density particle filter, a new algorithm of survive particle importance sampling is presented. For every particle, the algorithm exploits likelihood between latest measurements, and the particle chooses one measurement of the set of measurement to obtain importance distribution by update particle, and draws samples of survive particle from the importance distribution. The presented algorithm reduced degeneration of particle efficiently. In simulation scenario of 3 targets tracking, the optimal sub-pattern assignment metric of particle probability hypothesis density filter, which adopted the presented survive particle importance sample method, decreases about 70Km when targets model used in target tracking method is different from actual targets model. The proposed method enhances the robustness of multitarget tracking of particle probability hypothesis density filter remarkably.