Robust Wireless Localization in UAV Swarm Networks: A Deep-Graph-Generator-Assisted Convex Optimization Approach

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 a...

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Bibliographic Details
Published in:IEEE internet of things journal Vol. 12; no. 20; pp. 41664 - 41677
Main Authors: Chen, Yu-Jia, Huang, Hai-Yan, Chen, Min-Wei, Ku, Meng-Lin
Format: Journal Article
Language:English
Published: Piscataway IEEE 15.10.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Anomaly detection
Autonomous aerial vehicles
Convex hulls
convex optimization
Convexity
deep learning
Global Positioning System
Global positioning systems
GPS
Graphical representations
Knapsack problem
Localization
Location awareness
Maximum likelihood estimation
Measurement errors
Optimization
received signal strength (RSS)
Robustness
Signal strength
Uncertainty
unmanned aerial vehicle (UAV)
Unmanned aerial vehicles
wireless localization
Wireless sensor networks
ISSN:2327-4662, 2327-4662
Online Access:Get full text
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Summary: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.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3591308