A proposal for UAV clustering-based relative localization in GNSS-denied environments

To ensure unmanned aerial vehicle (UAV) swarm safety in areas lacking satellite navigation, accurate localization must be achieved through alternative methods such as relative positioning and clustering. A key challenge involves balancing higher localization accuracy with lower computational complexity, especially in urban environments. Additionally, maintaining up-to-date data transmission to ground stations is critical for real-time localization in dynamic scenarios.This paper proposes a cooperative relative localization scheme based on clustering for UAV swarms. The proposal employs the degree centrality metric to form clusters and connectivity information for cluster head selections, and balances intra-cluster cooperation with inter-cluster packet loss, ensuring the freshness of transmitted data.A first evaluation of the proposed scheme allows us to venture that it will achieve better performance (in terms of mean square error) compared to state-of-the-art techniques for relative localization of UAV swarms.