A novel infrared target extraction algorithm based on particle swarm optimization particle filter (PSOPF) was proposed. The problem of infrared target extraction was analyzed and solved in the view of state estimation. In the framework of particle filter, the threshold state space on the gray-variance weighted information entropy and the gray value of each pixel was established. Particle swarm optimization was introduced to construct the state transition model. The observation model based on extraction results evaluation function was constructed, which integrated gray, entropy, gradient and spatial distribution of pixels. Finally, the weighted average of all the particles was used as target extraction threshold. The experiment results prove that the proposed algorithm is effective and robust.