Paper Info
Title
Multi-UAV Cooperative Target Tracking Based on Swarm Intelligence
Abstract
In recent years, unmanned aerial vehicles (UAV) have been widely adopted to support complex target tracking tasks for military and civilian applications, especially in open and unknown environments. In practical cases, the moving trajectory of the target cannot be known to the UAVs in advance, which brings great challenges to UAVs to realize real-time and effective tracking. In addition, the limited tracking ability of a single UAV can hardly meet the requirements of a high tracking success rate. To deal with these problems above, this paper establishes a multi-UAV cooperative target tracking system. Besides, a deep reinforcement learning (DRL) based algorithm is designed to enable UAVs to make flight action decisions intelligently to track the moving air target, according to the past and current position information of the target only. To further increase the detection coverage of the UAV network when tracking, spatial information entropy is introduced to the reward designing in this algorithm. Simulation results validate that the proposed algorithm yields impressive target tracking performances, and significantly outperforms several common DRL baselines in terms of the tracking success rate. The convergence of the algorithm is also verified by the simulations.
Year
DOI
Venue
2021
10.1109/ICC42927.2021.9500771
IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021)
DocType
ISSN
Citations 
Conference
1550-3607
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Zhaoyue Xia100.34
Jun Du2215.67
Chunxiao Jiang32064161.92
Jingjing Wang437333.83
Yong Ren5104499.99
Gang Li629851.27