In the field of target tracking, particle filter technology has the advantage of dealing with nonlinear non-Gaussian problems. However, when the standard particle filter solves the degradation phenomenon by using the resampling method, the particle depletion problem will occur, resulting in unstable filter precision. To solve this problem, the algorithm uses the differential evolution bat algorithm to improve the particle filter. In this study, the particle is characterized as a bat individual. The bat population adjusts the frequency, loudness, and pulse emissivity, and the current optimal bat individual searches in the target image area, and can dynamically decide whether to use global search or local search to improve the particle. The overall quality and reasonable distribution; the introduction of differential evolution strategies can enhance the ability of bat individuals to jump out of local optimum. In order to verify the optimization performance of the proposed algorithm, the performances of the proposed algorithm and the standard particle filter algorithm are compared. Experimental results show that the filter performance of the proposed algorithm is better than the standard particle filter algorithm.