Optimized Deployment of HAPS Systems for GNSS Localization Enhancement in Urban Environments

TL;DR

This paper introduces a genetic algorithm-based framework to optimize the number and placement of high altitude platform stations (HAPS) for improving GNSS localization accuracy in dense urban environments, considering practical constraints.

Contribution

It presents a novel metaheuristic optimization method tailored for joint HAPS deployment and placement, addressing the nonconvex problem with environment-dependent CRLB constraints.

Findings

Successfully minimizes HAPS count for desired CRLB threshold

Identifies cost-effective HAPS configurations with optimal localization performance

Demonstrates scalability and practical applicability in urban scenarios

Abstract

While high altitude platform stations (HAPS) have been primarily explored as network infrastructure for communication services, their advantageous characteristics also make them promising candidates for augmenting GNSS localization. This paper proposes a metaheuristic framework to jointly optimize the number and placement of HAPS for GNSS enhancement in dense urban environments, considering practical constraints such as elevation masks, altitude limits, and ray-traced visibility from 3D city models. The problem is highly nonconvex due to the discrete HAPS count and the environment-dependent 3D Cramer-Rao lower bound (CRLB). To address this, we develop a tailored version of the adaptive special-crowding distance non-dominated sorting genetic algorithm II (ASDNSGA-II). Simulations show the method successfully identifies the minimum number of HAPS needed to satisfy a CRLB threshold and selects the configuration with the lowest CRLB within that minimum, offering a cost-effective and scalable solution for future HAPS-aided positioning systems.