基于深度学习网络模型的端到端航迹关联

doi:10.11896/jsjkx.190400037

计算机科学 ›› 2020, Vol. 47 ›› Issue (3): 200-205.doi: 10.11896/jsjkx.190400037

基于深度学习网络模型的端到端航迹关联

黄虹玮^1,2,刘玉娇³,沈卓恺¹,张少伟³,陈志敏²,高阳¹

(南京大学计算机科学与技术系南京210093)¹;
(中国卫星海上测控部江苏无锡214431)²;
(上海航天控制技术研究所上海201109)³

收稿日期:2019-04-08 出版日期:2020-03-15 发布日期:2020-03-30
通讯作者: 高阳(gaoy@nju.edu.cn)
基金资助:
国家自然科学基金(61432008,U1435214)

End-to-end Track Association Based on Deep Learning Network Model

HUANG Hong-wei^1,2,LIU Yu-jiao³,SHEN Zhuo-kai¹,ZHANG Shao-wei³,CHEN Zhi-min³,GAO Yang¹

(Department of Computer Science and Technology, Nanjing University, Nanjing 210093, China)¹;
(China Satellite Maritime TT&C Department, Wuxi, Jiangsu 214431, China)²;
(Shanghai Aerospace Control Technology Institute, Shanghai 201109, China)³

Received:2019-04-08 Online:2020-03-15 Published:2020-03-30
About author:HUANG Hong-wei,born in 1986,Ph.D.His main research interests include stream data mining,online learning,and few-shot learning. GAO Yang,Ph.D,professor,deputy director.His research interests include artificial intelligence and machine learning.
Supported by:
This work was supported by the National Natural Science Foundation of China (61432008, U1435214).

摘要/Abstract

摘要： 为提高雷达数据处理中航迹关联的智能性,充分利用目标的特征信息,并简化系统处理流程,提出了一种基于深度学习网络模型的端到端航迹关联算法。首先分析了基于神经网络的航迹关联存在样本细节少、处理流程繁杂的问题,然后提出了端到端的深度学习模型。该模型根据航迹关联数据的处理特征,改进了卷积神经网络结构用于特征提取,充分利用了长短期记忆网络对历史信息和将来信息的处理能力,并分析了前后航迹的关联性。在对原始数据进行卡尔曼滤波后,将全部航迹信息特征作为输入,并由基于卷积神经网络特征提取的长短期记忆深度神经网络模型直接输出航迹关联结果。仿真结果表明,提出的模型可以充分学习推演目标的多个特征信息,具有较高的航迹关联准确率,对航迹关联的智能化分析具有一定的参考价值。

关键词: 长短期记忆网络, 航迹关联, 卷积神经网络, 深度学习

Abstract: In order to improve the intelligence of track association in radar data processing,make full use of the characteristic information of the target and simplify the processing flow,an end-to-end track association algorithm based on deep learning network model was proposed.Firstly,this paper analyzed the problem that the track correlation based on neural network has few sample details and complex processing flow.Then,it proposed an end-to-end deep learning model,which takes all the track information features as input.According to the processing characteristics of track correlation data,the convolutional neural networks structure is improved for feature extraction,and the processing ability of long short-term memory neural network for historical information and future information is fully utilized to analyze the correlation of track before and after.After the original data is processed with Kalman filtering,the final track correlation results are directly output through the long short-term memory deep neural network model based on the convolutional neural networks features extracting.In this paper,the precision,recall and accuracy were set to verify the performance of the track association model.The simulation results show that the proposed model can fully learn multiple feature information of the target and has a high track association accuracy,which has reference value for the intelligent analysis of track association.

Key words: Convolutional neural networks, Deep learning, Long short-term memory, Track association

中图分类号:

TP391

黄虹玮,刘玉娇,沈卓恺,张少伟,陈志敏,高阳. 基于深度学习网络模型的端到端航迹关联[J]. 计算机科学, 2020, 47(3): 200-205. https://doi.org/10.11896/jsjkx.190400037

HUANG Hong-wei,LIU Yu-jiao,SHEN Zhuo-kai,ZHANG Shao-wei,CHEN Zhi-min,GAO Yang. End-to-end Track Association Based on Deep Learning Network Model[J]. Computer Science, 2020, 47(3): 200-205. https://doi.org/10.11896/jsjkx.190400037

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基于深度学习网络模型的端到端航迹关联

End-to-end Track Association Based on Deep Learning Network Model

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