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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Kit Chan, Yee
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Computer Science & IT Engineering

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Robust UAV localization of ground sensors in urban environments via path loss refinement and geometric selection Elngar, Ahmed M. A. A.; Lim, Heng Siong; Kit Chan, Yee; Bakhuraisa, Yaser Awadh; Wahidah, Ida
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp412-428

Abstract

Localizing ground sensors with unmanned aerial vehicles (UAVs) in dense urban environments is challenging because multipath and non-line-of-sight (NLoS) propagation distorts path loss (PL) measurements. This paper proposes a two-stage UAV localization framework that refines PL data and selects geometrically stable waypoint subsets before position estimation. In stage 1, PL samples are spatially smoothed by averaging measurements at neighboring UAV waypoints to reduce localized fluctuations. In stage 2, waypoint subsets are filtered using non-collinearity and non-adjacency constraints, and sensor positions are estimated using weighted least squares (WLS) and particle swarm optimization (PSO), with final estimates averaged across valid subsets. Wireless InSite ray-tracing simulations show that the framework reduces mean absolute error (MAE) from over 150 m to approximately 8.5 m. The proposed approach improves the practicality of UAV-assisted localization for urban internet of things (IoT) sensor deployments.
Co-Authors Bakhuraisa, Yaser Awadh Elngar, Ahmed M. A. A. Ida Wahidah Hamzah Lim, Heng Siong