摘要
Abstract
In the context of cross-spatiotemporal multi-source heterogeneous data association,the cross-spatiotemporal situation of targets arising from large temporal spans poses a particularly prominent challenge.Both traditional prediction-discrimination-based and novel deep embedding model-based association methods struggle to apply effectively,highlighting an urgent need for solutions.Addressing the issue of cross-spatiotemporal and multi-source heterogeneous data association for targets under the condition of large spatiotemporal shifts,a method using mandatory inspection and evidence augmentation-based is proposed.This method considers the spatiotemporal constraints of targets and leverages the shared information across multi-source heterogeneous data,including attributes,speeds,positions,headings,etc.,to construct mandatory inspection and evidence augmentation association features,thereby enabling association decisions.Experimental results based on simulation data demonstrate that,compared with single-dimensional association methods,the proposed method significantly improves the hit rate and recall rate of cross-spatiotemporal multi-source heterogeneous data association,the hit rates of Hits@1 and Hits@5 are increased by at least 11%and 4%respectively,reaching 99%and 100%;the recall rates of R@1 and R@5 are increased by at least 11%and 9%respectively,reaching 99%and 87%.Furthermore,the constructed cross-spatiotemporal multi-source heterogeneous data association framework allows users to rapidly extend new association features,such as semantic association features and appearance association features,showcasing excellent scalability.关键词
数据关联/跨时空数据关联/多源异构数据关联/强制检验/证据增强Key words
data association/cross-spatiotemporal data association/multi-source heterogeneous data association/mandatory inspection/evidence augmentation分类
信息技术与安全科学
