Abstract: An improved scorpion-simulated method for estimating the position of a vibration source is proposed, to address the issue of limited positioning accuracy due to incomplete scorpion-simulated vibration source positioning models. Based on the different attenuation rates of vibration signal frequency components with increasing distance, the method first improves the existing scorpion vibration source positioning mechanism to address the lack of distance estimation in current models. Then, it extracts the features of the vibration source location based on the improved model and constructs a complete feature database. Next, the K-means method is used to create sub-databases to reduce data matching time. Finally, the KNN(K-Nearest Neighbor)algorithm is used to estimate the position of the vibration source. To verify the effectiveness of the proposed method, tests are conducted on a dataset containing 64 locations of personnel stepping signals in a concentric circle with an inner radius of 2 m and an outer radius of 6 m. The results show that the improved method for simulating scorpion vibration source positioning increases the average positioning accuracy by 0. 439 6 m compared to the existing method and improves the positioning time by 31. 34% compared to the original matching method. Therefore, the improved method has achieved significant enhancements in both positioning accuracy and efficiency.

Key words: information processing technology, indoor positioning, scorpions, K-means clustering