Target recognition and tracking of group vehicles based on roadside millimeter-wave radar

被引：0

作者：

Li, Li ^{[1
]}

Wu, Xiao-Qiang ^{[1
]}

Yang, Wen-Chen ^{[2
,3
]}

Zhou, Rui-Jie ^{[1
]}

Wang, Gui-Ping ^{[1
]}

机构：

[1] School of Electronic and Control Engineering, Chang'an University, Xi'an

[2] National Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants Co.，Ltd, Kunming

[3] Yunnan Key Laboratory of Digital Communications, Kunming

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2024年 / 54卷 / 07期

关键词：

filter; Gaussian mixture probability hypothesis density; group vehicles; intelligent transportation; millimeter wave radar; target recognition; vehicle tracking;

D O I：

10.13229/j.cnki.jdxbgxb.20221174

中图分类号：

学科分类号：

摘要：

A method of group vehicle recognition and tracking based on roadside millimetre-wave radar was proposed to improve roadway traffic detection accuracy. Based on pre-processed detection data of millimetre-wave radar on multi-lane traffic flow in an urban arterial road，a Gaussian kernel-distance based spatial clustering algorithm with noise density（DBSCAN）was proposed to conduct spatio-temporal clustering of effective radar signals reflected by group vehicles. Then，a fusion algorithm of unscented Kalman filter （UKF） and linear Gaussian mixture probability hypothesis density（GMPHD）was proposed to improve tracking accuracy of group vehicles which move nonlinearly on the road. The algorithms were tested in simulation and onsite environment. Simulation results verified that the UK-GMPHD algorithm can accurately and stably track nonlinear moving vehicles. Results of onsite test showed that the kernel-distance based DBSCAN algorithm can solve the problem of classical algorithm effectively that the parameter tuning of feature vector was difficult to adjust. The UK-GMPHD algorithm reduced the root mean square error of target tracking in term of target distance，velocity and angle by 21.03%，23.41% and 20.67% in comparison with GMPHD algorithm. © 2024 Editorial Board of Jilin University. All rights reserved.

引用

页码：2104 / 2114

页数：10

共 17 条

[1] Jiang Q, Zhang L, Meng D., Target detection algo⁃ rithm based on MMW radar and camera Fusion[C], 2019 IEEE Intelligent Transportation Systems Con⁃ ference (ITSC), (2019)
[2] Brodeski D, Bilik I, Giryes R., Deep radar detector [C], 2019 IEEE Radar Conference, (2019)
[3] Wang Z, Miao X, Huang Z, Et al., Research of target detection and classification techniques using millime⁃ ter-wave radar and vision sensors, Remote Sens⁃ ing, 13, 6, pp. 1064-1086, (2021)
[4] Cao L, Wang T, Wang D, Et al., Lane determination of vehicles based on a novel clustering algorithm for intelligent traffic monitoring[J], IEEE Access, 8, pp. 63004-63017, (2020)
[5] Zhang Yu, Lin Yu-tian, Gao Li, Et al., Static target recognition and tracking based on Millimeter Wave Radar, 2021 the Annual Meeting of China Soci⁃ ety of Automotive Engineering, pp. 96-102, (2021)
[6] Ikram M, Ali M., 3D object tracking in millimeter-wave radar for advanced driver assistance systems [C], 2013 IEEE Global Conference on Signal and In⁃ formation Processing, pp. 723-726, (2013)
[7] Cui H, Wu J, Zhang J., 3D detection and tracking for on-road vehicles with a monovision camera and dual low-cost 4D mm wave radars, 2021 IEEE Inter⁃ national Intelligent Transportation Systems Confer⁃ ence, pp. 2931-2937, (2021)
[8] Ma J, Tian Z, Li Y, Et al., Vehicle tracking method in polar coordinate system based on radar and monocu⁃ lar camera[C], 2020 Chinese Control and Decision Conference(CCDC), pp. 93-98, (2020)
[9] Huang C, Wu X., Cooperative vehicle tracking using particle filter integrated with interacting multiple mod⁃ els[C], 2019 IEEE International Conference on Com⁃ munications, (2019)
[10] Liu H, Li N, Guan D, Et al., Data feature analysis of non-scanning multi target millimeter-wave radar in traffic flow detection applications, Sensors, 18, pp. 2756-2775, (2018)

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