A Metaheuristic Framework for Optimized HAPS-Aided Localization in Urban Areas

Abstract

High-altitude platform stations (HAPS), originally designed for communication services, can also provide structured signals of opportunity (SoOP) to augment the global navigation satellite system (GNSS). However, dense urban environments introduce severe blockage and non-line-of-sight (NLOS) conditions that undermine GNSS accuracy and render geometric placement metrics insufficient. To address this, we propose a metaheuristic framework for jointly optimizing the number and placement of HAPS under practical constraints by integrating high-fidelity 3D city models, ray-tracing, and multi-objective optimization to handle the discrete and highly non-convex design space. Three metaheuristic solutions based on distinct search principles are developed to efficiently explore the solution space, all demonstrating rapid convergence and consistently outperforming a greedy baseline, particularly in the low-to-moderate HAPS regime. For representative dense urban scenarios, we show that four HAPS are sufficient to satisfy an 18-m average 3D positioning error bound (PEB) threshold, while configurations with two to five HAPS achieve over 50\% reduction in mean and root mean square (RMS) PEB and up to 94\% and 87\% reduction in standard deviation and coefficient of variation (CV), respectively, compared to the satellite-only case. Diminishing returns are observed beyond six HAPS due to geometric redundancy, emphasizing the importance of optimized placement. The framework further demonstrates strong robustness and generalizability across diverse urban environments with varying building morphology and propagation conditions, establishing it as an effective and scalable solution for HAPS-assisted localization in realistic urban settings.