Volume 11 Issue 4
Aug.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(4): 517-529
LIANG Junli, TU Yu, MA Yunhong, et al. Task-driven flexible array beampattern synthesis for self-organized drone swarm[J]. Journal of Radars, 2022, 11(4): 517–529. doi: 10.12000/JR22130
Citation: LIANG Junli, TU Yu, MA Yunhong, et al. Task-driven flexible array beampattern synthesis for self-organized drone swarm[J]. Journal of Radars, 2022, 11(4): 517–529. doi: 10.12000/JR22130
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Task-driven Flexible Array Beampattern Synthesis for Self-organized Drone Swarm

doi: 10.12000/JR22130
  • 1.

    School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China

  • 2.

    Yangzhou Collaborative Innovation Research Institute Co., Ltd., Institute of Shenyang Aircraft Design and Research, Yangzhou 225000, China

  • 3.

    Key Laboratory of Complex Aviation Simulation System, Beijing 100076, China

Funds:  The National Nature Science Foundation of China (62271403), Key Research and Development Program of Shaanxi (2021SF-166)
More Information
  • Corresponding author: LIANG Junli, liangjunli@nwpu.edu.cn
  • Received Date: 2022-07-01
  • Accepted Date: 2022-08-12
  • Rev Recd Date: 2022-08-12
    • Available Online: 2022-08-15
  • Publish Date: 2022-08-23
    Abstract
  • This paper proposes a novel task-driven flexible array beampattern synthesis model for self-organized drone swarms according to their task requirements so that they can adjust their positions appropriately and point in a specific direction. First, we formulate the novel beampattern synthesis model using the drone-swarm antenna position and weight vector as the optimization variables and the maximum driving distance as the constraint. Then, the Lawson criterion is used to simplify the objective function, and the two kinds of optimization variables of antenna position and weight vector are reduced to a single kind of variable optimization problem of antenna position, alleviating the optimization difficulty caused by the usage of coupled variables. Simultaneously, auxiliary variables are introduced to separate the constraints from the complex objective function, and the Alternating Direction Method of Multipliers (ADMM）is used to slove the problem, which reduces the difficulty of solving a highly nonlinear optimization problem with constraints,. In addition, we extend this method to a scenario in which the provided Direction Of Arrival (DOA）of interest is imprecise. Simulation results show that the proposed method can obtain lower sidelobe levels than previous methods.

     

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