A Cooperative Localization Algorithm Based on Theory of Surveying Adjustment With Range Network for UAV Clusters

Cooperative localization distinguishes itself from traditional localization methods by leveraging internode measurements among unknown nodes. This study proposes a novel dual-stage algorithm grounded in the theory of surveying adjustment, which strategically integrates pairwise relative range measurements with cluster topological constraints. Initially, a range measurement error adjustment model is developed to mitigate range errors through cluster spatial relationship analysis. Subsequently, a theory of survey adjustment (TOSA)-based optimization algorithm is proposed to realize a globally optimal estimation while simultaneously refining range measurements through the inherent robustness of the cluster network. Furthermore, a complete Euclidean range matrix derived from the TOSA approach is effectively incorporated into the multidimensional scaling (MDS) framework, thereby significantly enhancing the precision of relative topology. Topology centroid derived from TOSA is introduced to provide additional reference for affine transformation in coordinate registration, extending the applicability of cooperative localization. Simulation results validate the algorithm’s efficacy and superiority in enhancing localization accuracy and maintaining topology integrity. Moreover, an extended investigation of time complexity provides valuable insights for future research endeavors in large-scale network configurations.