Research on Distributed Relative Localization Algorithms for UAV Cluster

Unmanned aerial vehicles (UAVs) are widely deployed due to their high flexibility, strong scalability, and costeffectiveness. Consequently, high-precision self-localization is paramount for swarm operations. In various applications such as formation control, the relative positioning of the swarm is often more critical than absolute positioning. Leveraging the advantages of dynamic partitioning and semi-definite programming (SDP), this paper proposes a novel distributed relative localization algorithm. Specifically, the UAVs is first partitioned into sub-blocks using a connectivity-based dynamic partitioning scheme. Then, the local geometric configurations are estimated via a weighted SDP algorithm and subsequently merged into a global geometric configuration based on statistical information. Simulation results demonstrate that the proposed algorithm outperforms the classical Multi-Dimensional Scaling (MDS) algorithm in terms of localization accuracy.