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Efficient Multi-Target Localization Using Dynamic UAV Clusters
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DOI：10.3390/s25092857.png)
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Abstract
En 中文
This paper proposes a dynamic unmanned aerial vehicle (UAV) clustering model for multi-target localization in complex 3D environments, where mobility-aware cluster formation is integrated to enhance collaborative localization accuracy. We derive the Cram & eacute;r-Rao lower bound (CRLB) for localization performance analysis under measurement and motion-induced uncertainties. To solve the NP-hard clustering problem, we develop the MDQPSO-ASA algorithm, which combines multi-swarm discrete quantum-inspired particle swarm optimization with adaptive simulated annealing, incorporating a repair mechanism to satisfy spatial and cardinality constraints. Simulation results demonstrate the algorithm's superiority in localization accuracy, computational efficiency, and adaptability to varying UAV/target scales compared to baseline methods. The developed algorithm provides an effective solution for resource-constrained collaborative localization tasks in practical scenarios.
Keywords:
multi-target localization
clustered UAV systems
combinatorial optimization
dynamic clustering
quantum-inspired optimization
Journal
IF:
3.5
Papers:
7.2W
Citations:
20.9W
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