TY - GEN
T1 - Object tracking algorithm for UAV autonomous Aerial Refueling
AU - Wu, Jiaju
AU - Yan, Jianguo
AU - Zhuoya, Wang
AU - Qu, Yaohong
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/28
Y1 - 2017/2/28
N2 - In order to improve the docking success rate in Automated Aerial Refueling (AAR), it is important to identify the receiver aircraft's receptacle for boom receptacle refueling (BRR). Meanshift tracking algorithm only considers the H component color statistics of the target area, lacking spatial information, could easily lead to inaccurate tracking. Besides, Meanshift tracking algorithm could easily lost target under occlusion conditions. To handle these situations, this paper proposes an improved Meanshift tracking algorithm based on color fusion and kernel function combined with Kalman filter (IMS-KF). In view of lacking color component, use RGB linear fusion. In view of lacking spatial information, define the kernel function by setting different weight to pixels, on the basis of the distance from the center point of target to the current point. In view of occlusion conditions, use Kalman Filter algorithm to estimate the location of moving targets. Meanshift tracking results will determine whether use Kalman forecasting. We implemented this algorithm on F-16 simulation experiment platform and the results reveal that our method meets industrial real-time requirements and has a better tracking robustness under a complex environment.
AB - In order to improve the docking success rate in Automated Aerial Refueling (AAR), it is important to identify the receiver aircraft's receptacle for boom receptacle refueling (BRR). Meanshift tracking algorithm only considers the H component color statistics of the target area, lacking spatial information, could easily lead to inaccurate tracking. Besides, Meanshift tracking algorithm could easily lost target under occlusion conditions. To handle these situations, this paper proposes an improved Meanshift tracking algorithm based on color fusion and kernel function combined with Kalman filter (IMS-KF). In view of lacking color component, use RGB linear fusion. In view of lacking spatial information, define the kernel function by setting different weight to pixels, on the basis of the distance from the center point of target to the current point. In view of occlusion conditions, use Kalman Filter algorithm to estimate the location of moving targets. Meanshift tracking results will determine whether use Kalman forecasting. We implemented this algorithm on F-16 simulation experiment platform and the results reveal that our method meets industrial real-time requirements and has a better tracking robustness under a complex environment.
KW - Automated Aerial Refueling
KW - Improved Meanshift (IMS)
KW - Kalman Filter (KF)
KW - Real-time requirement
KW - Robustness
UR - http://www.scopus.com/inward/record.url?scp=85016814953&partnerID=8YFLogxK
U2 - 10.1109/IMCEC.2016.7867500
DO - 10.1109/IMCEC.2016.7867500
M3 - 会议稿件
AN - SCOPUS:85016814953
T3 - Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016
SP - 1665
EP - 1669
BT - Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016
A2 - Xu, Bing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016
Y2 - 3 October 2016 through 5 October 2016
ER -