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  2021, Vol. 2 Issue (2): 91-100    doi: 10.23919/ICN.2021.0011
Series On Data Driven Intelligence, Sustainability, And Systems     
Multitarget tracking control algorithm under local information selection interaction mechanism
Jiehong Wu*(),Jinghui Yang,Weijun Zhang,Jiankai Zuo
School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China
Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
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Abstract  

This study focuses on the problem of multitarget tracking. To address the existing problems of current tracking algorithms, as manifested by the time consumption of subgroup separation and the uneven group size of unmanned aerial vehicles (UAVs) for target tracking, a multitarget tracking control algorithm under local information selection interaction is proposed. First, on the basis of location, number, and perceived target information of neighboring UAVs, a temporary leader selection strategy is designed to realize the local follow-up movement of UAVs when the UAVs cannot fully perceive the target. Second, in combination with the basic rules of cluster movement and target information perception factors, distributed control equations are designed to achieve a rapid gathering of UAVs and consistent tracking of multiple targets. Lastly, the simulation experiments are conducted in two- and three-dimensional spaces. Under a certain number of UAVs, clustering speed of the proposed algorithm is less than 3 s, and the equal probability of the UAV subgroup size after group separation is over 78%.



Key wordsmultitarget tracking      time-consuming grouping      local information selection interaction      temporary leader selection strategy      subgroup size     
Received: 11 March 2021      Online: 19 August 2021
Corresponding Authors: Jiehong Wu     E-mail: wujiehong@sau.edu.cn
About author: Jiehong Wu received the master degree from Northeastern University in 2002, and the PhD degree from Northeastern University in 2008. Her main research interests include autonomous unmanned system collaborative control, secure communication, path optimization, and energy consumption optimization.|Jinghui Yang received the bachelor degree from Tangshan University in 2018, and is currently pursuing the master degree at the School of Computer Science, Shenyang Aerospace University. His main research interests include multi-UAV cooperative control, target tracking, and deep learning.|Weijun Zhang received the master degree from Northeastern University in 2004. His main research interests include automated stereoscopic warehouse, computer detection, and control.|Jiankai Zuo received the bachelor degree from Shenyang Aerospace University in 2020, and is currently pursuing the PhD degree in computer science and technology with Tongji University, China. His research interests include machine learning, data mining, and intelligent transportation systems.
Cite this article:

Jiehong Wu,Jinghui Yang,Weijun Zhang,Jiankai Zuo. Multitarget tracking control algorithm under local information selection interaction mechanism. , 2021, 2: 91-100.

URL:

http://icn.tsinghuajournals.com/10.23919/ICN.2021.0011     OR     http://icn.tsinghuajournals.com/Y2021/V2/I2/91

Fig. 1 Target escape model.
Fig. 2 Diagram of local information interaction
ParameterNN?tRm1m2krkcl
Value1828.40.50.50.30.20.5
Table 1 A-LISI simulation parameters.
Fig. 3 Target tracking process.
Fig. 4 UAV speed variation.
Fig. 5 Time consumption of clustering.
Fig. 6 Polarization index comparison.
NTime (s)
A-LISIIAPLAA
1.752.782.92
401.792.843.04
501.822.983.18
601.863.113.26
701.893.273.44
Table 2 Time comparison of cluster tracking.
Fig. 7 Contrast graph of the interval of the clustering.
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