电讯技术2026，Vol.66Issue(8)：1270-1275,6.DOI:10.20079/j.issn.1001-893x.250213004
基于深度三元组网络驱动的异步航迹关联算法
An Asynchronous Track-to-Track Association(T2TA)Algorithm Based on Deep Triplet Network
王贤圆1
作者信息
- 1. 西南电子技术研究所,成都 610036||复杂航空系统仿真全国重点实验室,成都 610036
- 折叠
摘要
Abstract
To solve the problem that current track-to-track association(T2TA)algorithms cannot distinguish similar tracks in dense scenarios,an asynchronous T2TA method based on deep triplet network is proposed.This algorithm is a two-stage method.In the first stage,on the basis of extracting temporal features by long short-term memory(LSTM),this algorithm utilizes deep metric learning with triplet loss to map tracks into a metric space.It can effectively increase the feature distance between positive and negative samples,thereby distinguishing the subtle feature of similar target.In the second stage,the association problem is taken as a binary classification task,and the track features are effectively classified by a binary classifier.Simulation experiments show that in dense target environment,the proposed method achieves an average correct association rate of 91.6%,which is 6.9%higher than that of traditional deep learning method.关键词
航迹关联/三元组网络/长短时记忆网络/深度度量学习Key words
track-to-track association/triplet network/long short-term memory/deep metric learning分类
信息技术与安全科学引用本文复制引用
王贤圆..基于深度三元组网络驱动的异步航迹关联算法[J].电讯技术,2026,66(8):1270-1275,6.
