Abstract: Particle filter based on particle swarm optimization algorithm is not precise and easily traps in local optimum, and it is difficult to satisfy the requirement of target tracking. To solve these problems, a novel particle swarm optimized particle filter is proposed. The method for updating particles is optimized by analyzing the cognition rule of individuals to groups, and the speed update strategy is improved. As a result, the superior particle velocity can mutation with a small probability, which improves the search ability. Meanwhile, due to the random evaluation for inferior particle, the diversity of filter is ensured. The simulation results show that this algorithm has the high precision, strong robustness and it's suitable for radar target tracking.