Robust Wireless Localization in UAV Swarm Networks: A Deep-Graph-Generator-Assisted Convex Optimization Approach - Chen, Yu-Jia; Huang, Hai-Yan; Chen, Min-Wei; Ku, Meng-Lin (IEEE internet of things journal, 2025)
Accurate and reliable localization is a prerequisite for unmanned aerial vehicle (UAV) swarm applications. However, conventional global positioning system or radio frequency-based localization systems often do not function effectively in highly dynamic and unstable mobile ad-hoc environments. This article proposes a new approach to localize UAVs accurately in unknown communication environments with anomalous global positioning system (GPS) reception, based on the received signal strength (RSS) between UAVs. The proposed approach is nontrivial, given the combinational nature of the considered problem and the requirement of high localization accuracy in the UAV application scenario. The key idea of the proposed approach is to solve the position mapping problem by refining a convex relaxation formulation that considers whether the target to be localized is inside or outside the convex hull formed by the anchors. In addition, a variational graph autoencoder is utilized to learn the latent representations for the undirected graph formed from the estimated position, which is then used to calculate the anomaly score. The optimal anchor node selection is obtained by solving a fractional knapsack problem that takes into account the anomaly score of different anchor combinations. Simulation results demonstrate that the proposed approach achieves higher detection and localization accuracy and is more robust to RSS measurement errors compared to the baseline schemes.