一种历史特征辅助的模糊数据关联算法

doi:10.19665/j.issn1001-2400.2021.02.012

西安电子科技大学学报 ›› 2021, Vol. 48 ›› Issue (2): 92-98.doi: 10.19665/j.issn1001-2400.2021.02.012

一种历史特征辅助的模糊数据关联算法

韩卓茜¹(),王锋¹(),陈沛²(),李卓伦³()

1.中国人民解放军战略支援部队信息工程大学数据与目标工程学院,河南郑州 450001
2.中国人民解放军61827部队,上海 200000
3.中国人民解放军战略支援部队信息工程大学信息系统工程学院,河南郑州 450001

收稿日期:2020-08-02 修回日期:2020-12-30 出版日期:2021-04-20 发布日期:2021-04-28
作者简介:韩卓茜(1996—),女,中国人民解放军战略支援部队信息工程大学硕士研究生,E-mail: 925345932@qq.com|王锋(1975—),男,副教授,博士,E-mail: wf32018@163.com|陈沛(1989—),男,助理研究员,博士,E-mail: clevercpei@126.com|李卓伦(1995—),男,中国人民解放军战略支援部队信息工程大学硕士研究生,E-mail: 729242979@qq.com
基金资助:
国家自然科学基金青年项目(61501513)

Fuzzy data association algorithm assisted by historical features

HAN Zhuoxi¹(),WANG Feng¹(),CHEN Pei²(),LI Zhuolun³()

1. School of Data and Target Engineering,PLA Strategic Support Force Information Engineering University,Zhengzhou 450001,China
2. Unit 61827 of PLA,Shanghai 200000,China
3. School of Information System Engineering,PLA Strategic Support Force Information Engineering University,Zhengzhou 450001,China

Received:2020-08-02 Revised:2020-12-30 Online:2021-04-20 Published:2021-04-28

摘要/Abstract

摘要：

针对观测数据存在强模糊性和不确定性问题,提出了一种基于高分辨一维距离像历史特征辅助的模糊数据关联算法。首先,针对高分辨一维距离像的姿态、幅度以及时移敏感性问题,对其提取得到敏感性低的特征;然后,使用航迹起始的特征构建初始特征样本库及历史时刻的特征构建历史特征样本库,并实时更新特征样本库;采用区间熵权法确定特征权重,计算量测与目标的模糊隶属度,构建模糊矩阵;最后,基于最大模糊隶属度原则实现量测与目标的关联。实验结果表明,在目标的机动和非机动场景下,这种算法的关联性能均优于模糊关联算法,并且随着杂波密集程度的增大,两个算法的关联性能均逐渐降低,但所提算法的关联性能更好。

关键词: 高分辨一维距离像, 历史特征, 特征提取, 特征辅助, 区间熵权法, 模糊数据关联, 关联

Abstract:

Aiming at the problem of strong ambiguity and uncertainty in the observed data,the author proposes a fuzzy data association algorithm based on the historical features of high-resolution one-dimensional range profile.First,for the high-resolution one-dimensional range profile's attitude,amplitude,and time-shift sensitivity,feature extraction is performed to obtain low-sensitivity features.Then,the features of the track initiation are used to construct the initial feature sample database;the features of the historical moment are utilized to construct the historical feature sample database,and the feature sample database is updated in real time.The feature weight is obtained by the interval entropy weight method and the fuzzy membership of the measurement,and the target is calculated to construct a fuzzy matrix.Finally,fuzzy data association is completed based on the principle of maximum fuzzy membership.Experimental results show that,in both the maneuvering and non-maneuvering scenarios of the target,the association performance of the proposed algorithm is better than that of the fuzzy data association algorithm.And with the increase in the clutter density,the association performance of the two algorithms is gradually decreased,but the association performance of the proposed algorithm becomes better.

Key words: high resolution one-dimensional range profile, historical feature, feature extraction, feature aided, interval entropy weight method, fuzzy data association, association

中图分类号:

TN953

韩卓茜,王锋,陈沛,李卓伦. 一种历史特征辅助的模糊数据关联算法[J]. 西安电子科技大学学报, 2021, 48(2): 92-98.

HAN Zhuoxi,WANG Feng,CHEN Pei,LI Zhuolun. Fuzzy data association algorithm assisted by historical features[J]. Journal of Xidian University, 2021, 48(2): 92-98.

图/表 10

图1

表1

图2

图3

图4

图5

图6

图7

图8

图9

参考文献 15

[1]	SEA R G. An Efficient Suboptimal Decision Procedure for Associating Sensor Data with Stored Tracks in Real-time Surveillance Systems[C]//Proceedings of the 1971 IEEE Conference on Decision and Control. Piscataway:IEEE, 1971: 33-37.
[2]	BAR-SHALOM Y, TSE E. Tracking in A Cluttered Environment with Probabilistic Data Association[J]. Automatic, 1975,11(5):451-460.
[3]	CHANG K C, BAR-SHALOM Y. Joint Probabilistic Data Association for Multitarget Tracking with Possibly Unresolved Measurements and Maneuvers[J]. IEEE Transactions on Automatic Control, 1984,29(7):585-594.
[4]	REID D B. An Algorithm for Tracking Multiple Targets[J]. IEEE Transactions on Automatic Control, 1979,24(6):843-854.
[5]	STUBBERUD S C, KRAMER K A. Data Association for Multiple Sensor Types Using Fuzzy Logic[J]. IEEE Transactions on Instrumentation and Measurement, 2006,55(6):2292-2302.
[6]	康旭超, 何广军, 陈峰, 等. 密集杂波下的模糊数据关联多目标跟踪算法[J]. 探测与控制学报, 2019,41(4):56-61+65.
	KANG Xuchao, HE Guangjun, CHEN Feng, et al. Fuzzy Data Associated with Multi-target Tracking Algorithm Under Dense Elutter[J]. Journal of Detection and Control, 2019,41(4):56-61+65.
[7]	SATAPATHI G S, SRIHARI P. All Neighbor Fuzzy Relational Data Association for Multitarget Tracking in the Presence of ECM[C]// Proceedings of the 2016 IEEE Annual India Conference. Piscataway:IEEE, 2016: 7838869.
[8]	王树亮, 阮怀林, 张兴良. 目标多特征信息的模糊数据关联算法[J]. 火力与指挥控制, 2011,36(10):127-130.
	WANG Shuliang, RUAN Huailin, ZHANG Xingliang. A Fuzzy Data Association Algorithm Based on Target’s Multi-characteristic[J]. Fire Control and Command Control, 2011,36(10):127-130.
[9]	方琳琳, 周超, 王锐, 等. 昆虫目标雷达散射截面积特征辅助跟踪算法[J]. 雷达学报, 2019,8(5):598-605.
	FANG Linlin, ZHOU Chao, WANG Rui, et al. RCS Feature-aided Insect Target Tracking Algorithm[J]. Journal of Radars, 2019,8(5):598-605.
[10]	周毅. 基于高分辨一维距离像的目标特征提取及融合识别研究[D]. 成都: 电子科技大学, 2018.
[11]	司进修. 基于深度学习的雷达一维距离像目标识别方法研究与实现[D]. 成都: 电子科技大学, 2019.
[12]	LIAO K, SI J, ZHU F, et al. Radar HRRP Target Recognition Based on Concatenated Deep Neural Networks[J]. IEEE Access, 2018,6:29211-29218.
[13]	王泓霖, 李伟, 许强, 等. 基于PSO-BP神经网络的HRRP目标识别[J]. 火力与指挥控制, 2019,44(12):39-44.
	WANG Honglin, LI Wei, XU Qiang, et al. High Resolution Range Profile Recognition Based on PSO-BP Neural Network[J]. Fire Control and Command Control, 2019,44(12):39-44.
[14]	黄柏圣, 江涛, 周伟光. 基于深度学习的HRRP舰船目标识别性能分析[J]. 现代雷达, 2020,42(2):54-57.
	HUANG Baisheng, JIANG Tao, ZHOU Weiguang. Performance Analysis of HRRP Ship Target Recognition Based on Deep Learning[J]. Modern Radar, 2020,42(2):54-57.
[15]	靳标, 苏涛, 李艳艳, 等. 一种高分辨距离像辅助的多目标跟踪算法[J]. 西安电子科技大学学报, 2016,43(1):1-6.
	JIN Biao, SU Tao, LI Yanyan, et al. Multi-target Tracking Algorithm Aided by a High Resolution Range Profile[J]. Journal of Xidian University, 2016,43(1):1-6.

一种历史特征辅助的模糊数据关联算法

Fuzzy data association algorithm assisted by historical features

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 15

相关文章 15

Metrics

本文评价

推荐阅读 10

[1]	黄秋实,张雅声,冯飞. 多星敏感器协同观测的短弧关联算法[J]. 西安电子科技大学学报, 2021, 48(4): 64-72.
[2]	刘洁怡,公茂果,詹涛,李豪,张明阳. 一种深度神经网络多站雷达系统干扰鉴别方法[J]. 西安电子科技大学学报, 2021, 48(2): 133-138.
[3]	魏梓钰,杨曦,王楠楠,杨东,高新波. 互惠双向生成对抗网络用于跨模态行人重识别[J]. 西安电子科技大学学报, 2021, 48(2): 205-212.
[4]	张逸宸,水鹏朗. 一种改进的泊松点过程概率多假设跟踪方法[J]. 西安电子科技大学学报, 2021, 48(2): 27-34.
[5]	郭泽坤,田隆,韩宁,王鹏辉,刘宏伟,陈渤. 采用CNN-SSD的雷达HRRP小样本目标识别方法[J]. 西安电子科技大学学报, 2021, 48(2): 7-14.
[6]	李翠芸,李洋,姬红兵,石仁政. 一步数据关联GLMB扩展目标跟踪算法[J]. 西安电子科技大学学报, 2020, 47(5): 137-143.
[7]	杨宏宇,张旭高. 一种网络安全态势自适应预测模型[J]. 西安电子科技大学学报, 2020, 47(3): 14-22.
[8]	王晓华,要鹏超,马丽萍,王文杰,张蕾. 车间环境下机器人语音控制的特征提取算法[J]. 西安电子科技大学学报, 2020, 47(2): 16-22.
[9]	王红艳,朱利民,张潘杰,李金屏. 一种利用外轮廓配准的活塞侧面缺陷检测方法[J]. 西安电子科技大学学报, 2019, 46(5): 75-83.
[10]	孙颖,吕慧芬,张雪英,马江河. 情感维度下的深度情感关联模型[J]. 西安电子科技大学学报, 2019, 46(5): 24-30.
[11]	郭睿,臧博,彭树铭,邢孟道. 高分辨InSAR中的城市高层建筑特征提取[J]. 西安电子科技大学学报, 2019, 46(4): 137-143.
[12]	刘道华,崔玉爽,赵岩松,宋玉婷,王景慧. 一种改进卷积神经网络的教学图像检索方法[J]. 西安电子科技大学学报, 2019, 46(3): 52-58.
[13]	杨建功,汪西莉,刘侍刚. 融合谱-空域信息的DBM高光谱图像分类方法[J]. 西安电子科技大学学报, 2019, 46(3): 109-115.
[14]	苏小凡,肖瑞,朱明哲. 一种多分量调频信号瞬时频率估计方法[J]. 西安电子科技大学学报, 2019, 46(1): 51-56.
[15]	王欣远,肖嵩,李磊,焦玲玲. 融合ELM和相关滤波的鲁棒性目标跟踪算法[J]. 西安电子科技大学学报, 2019, 46(1): 57-63.