This study addresses the optimization of collaborative navigation for large-scale aircraft clusters in different air domains scenarios,focusing on clustering-based optimization for improving cooperative localization.Initially,the aircraft clusters are partitioned based on different air domains.Building on this,a clustering scheme is proposed to optimize localization.Subsequently,a cooperative localization model tailored to clustering scenarios is developed and achieves significant improvements in the localization performance of low-air-domain aircrafts in Global Navigation Satellite System(GNSS)-denial environment,increasing positioning accuracy by 25.60%compared to unoptimized systems.Furthermore,the clustering variance ratio is improved by approximately 49.53%compared to fixed clustering methods,demonstrating the effectiveness of the proposed approach.Compared to traditional cooperation methods of clusters,this cooperative clustering optimization scheme enhances both clustering performance efficiency and localization accuracy in large-scale cross-domain systems.It's better suited to address challenges like GNSS-denial in low air scenarios,offering potential for applications.
Different Air Domains ScenarioAircraft ClustersClustering-based OptimizationCoop-erative LocalizationMultidimensional ScalingTime Difference of Arrival
维普
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