Dynamic Grouping Method for Multinode Cooperative Localization Based on Improved K-Means

In a dynamic battlefield environment that is increasingly tending to digitalization and informatization, unmanned aerial vehicle (UAV) clusters with efficient collaborative capabilities have become the mainstream trend of future war patterns. The level of positioning accuracy of UAVs determines the accuracy and efficiency of mission execution, thus affecting the success rate of combat. However, in multinode cooperative localization, the communication-over-distance problem has become a key technical challenge that needs to be solved urgently. In this study, a dynamic grouping method based on K-means for multinode cooperative localization is proposed, setting constraints that better fit real-world scenarios to guide the multinodes to cluster grouping. Dynamic grouping cooperative localization simulation experiments are conducted for 20 and 50 node cooperative systems, and the results show that the dynamic grouping method based on improved K-means improves the localization accuracy by $38.61 \%$ on average compared with the fixed grouping. Moreover, the average time of grouping is not more than 0.57 s, and the maximum time is not more than 0.85 s.