摘要
在多目标被动定位过程中,目标批次划分的正确与否影响着定位解算结果的准确性。针对这一问题,本文提出了一种多目标数据联合关联算法。对目标特征数据进行关联,利用灰色系统理论和系统聚类分析方法判断目标间的相似程度,对于不确定的集合再次进行方位数据关联。由仿真结果可知,2种算法进行联合关联可以弥补单一方法带来的不足,提高目标关联正确率。
In the process of multi-target passive positioning,the correctness of the target batch division affects accuracy of the positioning solution results.In response to this problem,this paper studies a multi-objective data joint association algorithm.First,the target feature data is correlated,and the similarity between the targets is judged by using the gray system theory and system cluster analysis method.For the uncertain set,the orientation data association is performed again.It can be seen from the simulation results that the joint association of the two algorithms can make up for the shortcomings brought by a single method and improve the accuracy of target association.
作者
孙寒涛
SUN Hantao(No.92493 Unit of PLA,Huludao 125000,China)
出处
《应用科技》
CAS
2020年第3期74-79,共6页
Applied Science and Technology
基金
水声技术重点实验室稳定支持课题(JCKYS2019604SSJS011)。
关键词
方位关联
特征关联
多目标
多观测节点
灰色关联
自适应熵权
方位-特征联合关联
聚类分析
orientation association
feature association
multi-target
multiple node
gray correlation
adaptive entropy weight
orientation-feature association
cluster analysis
