Abstract: For the particle degradation and particle scarcity of the resampling process of traditional Particle Filter Algorithm, a wind driven optimization particle filter algorithm was proposed in this paper. The ideology of wind driven optimization was introduced in this algorithm, before resampling, the particle is optimized by wind driven optimization firstly. Due to wind driven optimization algorithm incorporated the latest observation into the particle evolution formulas, most of the particles after wind driven optimization optimized moved to the dense area of posterior probability distribution and gathered in the vicinity of the optimal particle. Thus decrease the probability of abandoning high weight particles, and alleviate the sample impoverishment problem. Through the analysis of using improved particle filter algorithm onto nonlinear target tracking model, and compared with traditional filter algorithm and particle swarm particle filter algorithm. The simulation result proves that the improved algorithm decreases variable error. Thereby, this improved particle filter algorithm has better filtering performance.