Volume 13 Issue 3
Jun.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(3): 629-645
CHEN Hui, BIAN Binchao, LIAN Feng, et al. A novel method for tracking complex maneuvering star convex extended targets using transformer network[J]. Journal of Radars, 2024, 13(3): 629–645. doi: 10.12000/JR24031
Citation: CHEN Hui, BIAN Binchao, LIAN Feng, et al. A novel method for tracking complex maneuvering star convex extended targets using transformer network[J]. Journal of Radars, 2024, 13(3): 629–645. doi: 10.12000/JR24031
shu

A Novel Method for Tracking Complex Maneuvering Star Convex Extended Targets Using Transformer Network

DOI: 10.12000/JR24031
  • 1.

    School of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China

  • 2.

    School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Funds:  The National Natural Science Foundation of China (62163023, 61873116, 62173266, 62366031), The Industrial Support Project of Education Department of Gansu Province (2021CYZC-02), The Special Funds Project for Civil-Military Integration Development of Gansu Province in 2023, The Key Talent Project of Gansu Province in 2024
More Information
  • Corresponding author: CHEN Hui, chenh@lut.edu.cn; BIAN Binchao, bianbinchao@lut.edu.cn
  • Received Date: 2024-02-29
  • Rev Recd Date: 2024-04-10
    • Available Online: 2024-04-17
  • Publish Date: 2024-05-10
    Abstract
  • To address the challenges in tracking complex maneuvering extended targets, an effective maneuvering extended target tracking method was proposed for irregularly shaped star-convex using a Transformer network. Initially, the alpha-shape algorithm was used to model the variations in the star-convex shape. In addition, a recursive approach was proposed to estimate the irregular shape of an extended target by detailed derivation in the Bayesian filtering framework. This approach accurately estimated the shape of a static star convex extended target. Moreover, through the structural redesign of the target state transition matrix and the real-time estimation of the maneuvering extended target’s state transition matrix using a transformer network, the accurate tracking of complex maneuvering targets was achieved. Furthermore, the real-time tracking of star convex maneuvering extended targets was achieved by fusing the estimated shape contours with motion states. This study focused on constructing certain complex maneuvering extended target tracking scenarios to assess the performance of the proposed method and the comprehensive estimation capabilities of the algorithm considering both shapes and motion states using multiple performance indicators.

     

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