In this paper,an algorithm for solving the multi-target correlation and co-location problem of aerial-ground heterogeneous system is investigated.Aiming at the multi-target correlation problem,the fusion algorithm of ...In this paper,an algorithm for solving the multi-target correlation and co-location problem of aerial-ground heterogeneous system is investigated.Aiming at the multi-target correlation problem,the fusion algorithm of visual axis correlation method and improved topological similarity correlation method are adopted in view of large parallax and inconsistent scale between the aerial and ground perspectives.First,the visual axis was preprocessed by the threshold method,so that the sparse targets were initially associated.Then,the improved topological similarity method was used to further associate dense targets with the relative position characteristics between targets.The shortcoming of dense target similarity with small di®erence was optimized by the improved topological similarity method.For the problem of colocation,combined with the multi-target correlation algorithm in this paper,the triangulation positioning model was used to complete the co-location of multiple targets.In the experimental part,simulation experiments and°ight experiments were designed to verify the e®ectiveness of the algorithm.Experimental results show that the proposed algorithm can e®ectively achieve multi-target correlation positioning,and that the positioning accuracy is obviously better than other positioning methods.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61876187 and 61806217.
文摘In this paper,an algorithm for solving the multi-target correlation and co-location problem of aerial-ground heterogeneous system is investigated.Aiming at the multi-target correlation problem,the fusion algorithm of visual axis correlation method and improved topological similarity correlation method are adopted in view of large parallax and inconsistent scale between the aerial and ground perspectives.First,the visual axis was preprocessed by the threshold method,so that the sparse targets were initially associated.Then,the improved topological similarity method was used to further associate dense targets with the relative position characteristics between targets.The shortcoming of dense target similarity with small di®erence was optimized by the improved topological similarity method.For the problem of colocation,combined with the multi-target correlation algorithm in this paper,the triangulation positioning model was used to complete the co-location of multiple targets.In the experimental part,simulation experiments and°ight experiments were designed to verify the e®ectiveness of the algorithm.Experimental results show that the proposed algorithm can e®ectively achieve multi-target correlation positioning,and that the positioning accuracy is obviously better than other positioning methods.
