UAV-Assisted Localization of Ground Nodes in Urban Environments Using Path Loss Measurements
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| Argitaratua izan da: | Drones vol. 9, no. 6 (2025), p. 450-466 |
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| Egile nagusia: | |
| Beste egile batzuk: | , , |
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MDPI AG
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| Sarrera elektronikoa: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3223899845 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-446X | ||
| 024 | 7 | |a 10.3390/drones9060450 |2 doi | |
| 035 | |a 3223899845 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Bakhuraisa Yaser |u Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia; 1211400159@student.mmu.edu.my (Y.B.); ykchan@mmu.edu.my (Y.K.C.) | |
| 245 | 1 | |a UAV-Assisted Localization of Ground Nodes in Urban Environments Using Path Loss Measurements | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper proposes a distance estimation error reduction framework to improve ground node localization accuracy in urban environments using an unmanned aerial vehicle (UAV) and path loss measurements. The primary goal of the framework is to bound distance estimation errors arising from inherent inaccuracies in path loss measurements. A k-means clustering algorithm is first applied to identify the region in which the ground node is located. Then, an analytical approach is used to select UAV waypoints. Moreover, a mean-based exponential smoothing approach is employed to refine the path loss measurements of the selected waypoints to mitigate the effects of multipath components that introduce significant errors in distance estimation. Finally, two estimators, maximum likelihood (ML)-based and semidefinite programming (SDP)-based relaxation, are employed to estimate the ground node’s location, validating the effectiveness of the proposed framework. Evaluations using ray tracing simulation data demonstrate a notable improvement in localization accuracy. The proposed framework effectively bounds the distance estimation errors and significantly reduces overall localization errors compared to conventional unbounded methods. Moreover, both estimators with the proposed framework achieve comparable localization accuracy, highlighting the framework’s capability to address key challenges in ML-based localization. | |
| 653 | |a Ray tracing | ||
| 653 | |a Global positioning systems--GPS | ||
| 653 | |a Accuracy | ||
| 653 | |a Semidefinite programming | ||
| 653 | |a Urban environments | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Radio frequency | ||
| 653 | |a Communication | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Clustering | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Nodes | ||
| 653 | |a Waypoints | ||
| 653 | |a Error reduction | ||
| 653 | |a Estimators | ||
| 653 | |a Algorithms | ||
| 653 | |a Localization | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Vector quantization | ||
| 700 | 1 | |a Lim, Heng Siong |u Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia; 1211400159@student.mmu.edu.my (Y.B.); ykchan@mmu.edu.my (Y.K.C.) | |
| 700 | 1 | |a Chan, Yee Kit |u Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Melaka 75450, Malaysia; 1211400159@student.mmu.edu.my (Y.B.); ykchan@mmu.edu.my (Y.K.C.) | |
| 700 | 1 | |a Hilman Muhammad |u Faculty of Computer Science, Universitas Indonesia, Kampus Baru UI Depok, Jawa Barat 16425, Indonesia; muhammad.hilman@ui.ac.id | |
| 773 | 0 | |t Drones |g vol. 9, no. 6 (2025), p. 450-466 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223899845/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223899845/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223899845/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |