Hierarchical cooperative navigation strategy for drone swarm in multi-leader mode based on ranging bias compensation

Accurate measurement and transmission of inter-device information are pivotal for optimizing swarm cooperative navigation systems, enhancing positioning accuracy, and enabling efficient dynamic resource allocation. In multi-leader drone swarms, follower drones located beyond the direct measurement range of leader nodes often experience degraded positioning accuracy due to sensor limitations. To address this challenge, a novel hierarchical cooperative navigation strategy is proposed for drone swarm operating in multi-leader configurations. Based on systematic error analysis, ranging bias is identified as the primary error source that degrades swarm positioning accuracy. Mathematical modeling reveals the propagation mechanism of this bias across hierarchical levels, which informs the design of the information flow and the hierarchical cooperative filter. This networked hierarchical approach enables multi-level transmission of high-precision positioning data from leader drones, thereby improving the robustness of the cooperative navigation algorithm. Simulation results indicate that the proposed method can estimate the ranging bias with over 82% accuracy and effectively compensate for it. In physical drone experiments, a significant reduction in positioning errors for vice leaders and followers was observed with the proposed compensation strategy, confirming its engineering feasibility.