Distributed and Collaborative Localization for Swarming UAVs

In recent years, unmanned aerial vehicles (UAVs), especially swarming UAVs are widely deployed in a variety of Internet-of-Things (IoT) scenarios. Since UAVs' positions are essential for their collaboration, high-precision localization for swarming UAVs has attracted a lot of attention. Although the global positioning system (GPS) receiver has been widely integrated in UAV, it is not accurate enough and is prone to accidental or deliberate interferences. In this article, we propose a distributed and collaborative localization method for swarming UAVs that combines super multidimensional scaling (SMDS) and patch dividing/merging with GPS information. Specifically, the SMDS is first used to get the relative coordinates of the UAVs in each patch, then we merge relative map patches into a global map and transform the relative coordinates of the UAVs to their absolute coordinates. Furthermore, we propose a low-complexity algorithm that greatly reduces the computational complexity of SMDS with a large number of UAVs. Simulation results validate that with accurate enough angle measurements, the proposed SMDS localization algorithm outperforms the other MDS-based collaborative localization algorithms and can greatly improve the localization accuracy and robustness of swarming UAVs.