DocumentCode :
3047750
Title :
Extend Kalman Gaussian mixture probability hypothesis density filter based on radar and IR sensor fusion
Author :
Hao, Yanling ; Meng, Fanbin ; Qiao, Xiangwei ; Zhao, Ziyang ; Zhang, Congmeng ; Cai, Yifeng
Author_Institution :
Coll. of Autom., Harbin Eng. Univ., Harbin, China
fYear :
2010
fDate :
20-23 June 2010
Firstpage :
1582
Lastpage :
1586
Abstract :
In this paper, we proposed a method to fuse data from radar and IR sensor in Extend Kalman probability hypothesis density (EK-GMPHD) filter. Firstly the multi-target is estimated with infrared (IR) sensor using EK-GMPHD filter, and then the filtering results are fused with measurements from radar through sequential filter, in this way, the multi-target state is updated at the tracking system. Under false alarms, missed detections and dense targets environment, this method has a high reliability when tracking multi-target. Simulation experiments are presented to demonstrate the performance of the proposed method.
Keywords :
Gaussian processes; Kalman filters; optical tracking; radar; sensor fusion; target tracking; IR sensor fusion; extend Kalman Gaussian mixture probability; hypothesis density filter; multitarget tracking; radar; sequential filter; tracking system; Filtering; Fuses; Infrared sensors; Kalman filters; Radar measurements; Radar tracking; Sensor fusion; Sensor systems; State estimation; Target tracking; Extend Kalman filter; GMPHD filter; data fusion; multi-target tracking; random sets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation (ICIA), 2010 IEEE International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-5701-4
Type :
conf
DOI :
10.1109/ICINFA.2010.5512253
Filename :
5512253
Link To Document :
بازگشت