Extending Reliability of mmWave Radar Tracking and Detection via Fusion With Camera

被引:44
作者
Zhang, Renyuan [1 ]
Cao, Siyang [1 ]
机构
[1] Univ Arizona, Dept Elect & Comp Engn, Tucson, AZ 85721 USA
关键词
Millimeter wave radar; Kalman filtering; error bounds; multisensor systems; sensor fusion; fusion-EKF; homography estimation; multi-target tracking;
D O I
10.1109/ACCESS.2019.2942382
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, a new radar-camera fusion system is presented. The fusion system takes into consideration the error bounds of the two different coordinate systems from the heterogeneous sensors, and further a new fusion-extended Kalman filter is utilized to adapt to the heterogeneous sensors. Real-world application considerations such as asynchronous sensors, multi-target tracking and association are also studied and illustrated in this paper. Experimental results demonstrated that the proposed fusion system can realize a range accuracy of 0.29m with an angular accuracy of 0.013rad in real-time. Therefore, the proposed fusion system is effective, reliable and computationally efficient for real-time kinematic fusion applications.
引用
收藏
页码:137065 / 137079
页数:15
相关论文
共 42 条
[1]   Estimation of three-dimensional radar tracking using modified extended kalman filter [J].
Aditya, Prima ;
Apriliani, Erna ;
Arif, Didik Khusnul ;
Baihaqi, Komar .
INTERNATIONAL CONFERENCE ON MATHEMATICS: PURE, APPLIED AND COMPUTATION, 2018, 974
[2]   ViBe: A Universal Background Subtraction Algorithm for Video Sequences [J].
Barnich, Olivier ;
Van Droogenbroeck, Marc .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2011, 20 (06) :1709-1724
[3]   ST-DBSCAN: An algorithm for clustering spatial-temp oral data [J].
Birant, Derya ;
Kut, Alp .
DATA & KNOWLEDGE ENGINEERING, 2007, 60 (01) :208-221
[4]   Flux tensor constrained geodesic active contours with sensor fusion for persistent object tracking [J].
Bunyak, Filiz ;
Palaniappan, Kannappan ;
Nath, Sumit Kumar ;
Seetharaman, Gunasekaran .
Journal of Multimedia, 2007, 2 (04) :20-33
[5]  
Clark S, 1998, IEEE INT CONF ROBOT, P3697, DOI 10.1109/ROBOT.1998.681411
[6]   Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest [J].
Debes, Christian ;
Merentitis, Andreas ;
Heremans, Roel ;
Hahn, Juergen ;
Frangiadakis, Nikolaos ;
van Kasteren, Tim ;
Liao, Wenzhi ;
Bellens, Rik ;
Pizurica, Aleksandra ;
Gautama, Sidharta ;
Philips, Wilfried ;
Prasad, Saurabh ;
Du, Qian ;
Pacifici, Fabio .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (06) :2405-2418
[7]  
Dubrofsky E., 2009, THESIS
[8]  
Folster F., 2006, Proc. IEEE International Conference on Radar, P1
[9]  
García J, 2005, RADAR CONF, P796
[10]  
Gürcan Y, 2017, EUROP RADAR CONF, P73, DOI 10.23919/EURAD.2017.8249150