Adaptive Topology Construction for Heterogeneous UAV Clusters in Dynamic Localization Tasks

Unmanned aerial vehicle (UAV) technology is evolving from single-unit autonomy to complex swarm coordination. In highly heterogeneous scenarios, to address the challenges of cooperative localization and maintaining wireless sensor network connectivity, an adaptive clustering algorithm for localization tasks is proposed. This method integrates sensor task types with mobility trend similarities, while also considering key network stability factors such as topology duration, throughput, and end-to-end latency. During the clustering and cluster head selection process, the algorithm ensures that each sub-cluster meets localization requirements. Simulations demonstrate that, compared to the K-means and Group Trend Similarity-based Double-Head Clustering (GTSC) algorithms, the proposed method significantly improves topology duration, localization accuracy, and delay, particularly in large-scale swarms.