In order to solve the problems of limited dynamic range, high delay and motion blur in indoor high-precision positioning technology, this paper proposes a particle filter (PF) algorithm combined with the mean shift (Mean Shift, MS) strategy. Through the asynchronous data processing characteristics of the event vision sensor, a high-precision positioning and target tracking system is designed. The event vision sensor is based on recording changes in pixel light intensity. Compared with traditional imaging equipment, it has the advantages of low latency, high resolution, and wide dynamic range, and exhibits excellent performance in high-speed motion and complex lighting environments. Through an in-depth analysis of the positioning technologies supported by different types of receivers and their shortcomings, this paper proposes a new positioning method with event data as the core, and designs a solution to address the limitations of traditional positioning methods in terms of delay, interference and accuracy. Optimize the model, thereby significantly improving the accuracy, real-time performance and robustness of positioning. Experimental verification shows that the designed system can maintain centimeter-level positioning errors under a variety of dynamic conditions, and shows strong adaptability in fast target motion and high dynamic range scenarios. This paper combines the mean shift strategy with particle filtering to achieve efficient tracking of target trajectories in complex scenes by optimizing the particle distribution update mechanism. It further verifies the fast convergence and real-time performance of this method under dynamic conditions, providing indoor accuracy. The field of positioning and target tracking provides reliable theoretical basis and practical reference.