Multimodal Fusion and Dynamic Resource Optimization for Robust Cooperative Localization of Low-Cost UAVs
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| Publicado en: | Drones vol. 9, no. 12 (2025), p. 820-847 |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3286273121 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-446X | ||
| 024 | 7 | |a 10.3390/drones9120820 |2 doi | |
| 035 | |a 3286273121 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Liu Hongfu | |
| 245 | 1 | |a Multimodal Fusion and Dynamic Resource Optimization for Robust Cooperative Localization of Low-Cost UAVs | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a <sec sec-type="highlights"> What are the main findings? <list list-type="bullet"> <list-item> </list-item>Proposes a novel collaborative localization algorithm integrating a cross-modal attention mechanism to fuse vision, radar, and lidar data, significantly enhancing robustness in occluded and adverse weather conditions. <list-item> Proposes a dynamic resource optimization framework using integer linear programming, enabling real-time allocation of computational and communication resources to prevent node overload and improve system efficiency. </list-item> What are the implications of the main findings? <list list-type="bullet"> <list-item> </list-item>Demonstrates superior performance in realistic simulations, significant improvements in positioning accuracy, resource efficiency, and fault recovery, demonstrating strong potential for applications in complex tasks. <list-item> Provides a practical, low-cost system solution validated in complex scenarios, establishing a viable pathway for the engineering deployment of robust UAV swarms. </list-item> To overcome the challenges of low positioning accuracy and inefficient resource utilization in cooperative target localization by unmanned aerial vehicles (UAVs) in complex environments, this paper presents a cooperative localization algorithm that integrates multimodal data fusion with dynamic resource optimization. By leveraging a cross-modal attention mechanism, the algorithm effectively combines complementary information from visual, radar, and lidar sensors, thereby enhancing localization robustness under occlusions, poor illumination, and adverse weather conditions. Furthermore, a real-time resource scheduling model based on integer linear programming is introduced to dynamically allocate computational and communication resources, which mitigates node overload and minimizes resource waste. Experimental evaluations in scenarios including maritime search and rescue, urban occlusions, and dynamic resource fluctuations show that the proposed algorithm achieves significant improvements in positioning accuracy, resource efficiency, and fault recovery, demonstrating strong potential for applications in complex tasks, demonstrating its potential as a viable solution for low-cost UAV swarm applications in complex environments. | |
| 653 | |a Linear programming | ||
| 653 | |a Accuracy | ||
| 653 | |a Collaboration | ||
| 653 | |a Adaptability | ||
| 653 | |a Integer programming | ||
| 653 | |a Communication | ||
| 653 | |a Bandwidths | ||
| 653 | |a Evacuations & rescues | ||
| 653 | |a Optimization | ||
| 653 | |a Task complexity | ||
| 653 | |a Efficiency | ||
| 653 | |a Adaptation | ||
| 653 | |a Recovery | ||
| 653 | |a Attention | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Overloading | ||
| 653 | |a Data integration | ||
| 653 | |a Localization | ||
| 653 | |a Scheduling | ||
| 653 | |a Low cost | ||
| 653 | |a Real-time programming | ||
| 653 | |a Sensors | ||
| 653 | |a Resource scheduling | ||
| 653 | |a Design | ||
| 653 | |a Algorithms | ||
| 653 | |a Drones | ||
| 653 | |a Weather | ||
| 653 | |a Robustness (mathematics) | ||
| 653 | |a Resource utilization | ||
| 653 | |a Lidar | ||
| 653 | |a Kalman filters | ||
| 700 | 1 | |a Fu Yajing | |
| 700 | 1 | |a Ma Yangyang | |
| 700 | 1 | |a Zhang Wanpeng | |
| 773 | 0 | |t Drones |g vol. 9, no. 12 (2025), p. 820-847 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286273121/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286273121/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286273121/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |