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<p>The significant differences in the size, contour shape, and structural features of extended targets, as well as the dynamic changes under different sensor perspectives, pose significant challenges to extended target tracking (ETT). However, a general ETT framework that balances shape estimation accuracy and model complexity is still lacking. Thus, based on Gaussian process theory, this study investigates the adaptive adjustment of measurement model according to the target’s shape information distribution, and proposes a measurement-driven ETT algorithm. The proposed algorithm utilizes the distribution characteristics of real-time measurement density to capture the target’s shape information, and designs a strategy to dynamically adjust the number and positions of basis points, which more effectively represents the complex target shape. Simulation results show that the proposed algorithm significantly improves the estimation accuracy of the extended target by only using 1/3 of the number of basis points compared to existing algorithms, providing an efficient solution for ETT to balance the trade-off between accuracy and complexity.</p>

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Measurement-Driven Dynamic Basis Point-Adjusted Gaussian Process Algorithm for Extended Target Tracking

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Jiashi Shen,
  • Mengdie Xu,
  • Chaoqun Yang

摘要

The significant differences in the size, contour shape, and structural features of extended targets, as well as the dynamic changes under different sensor perspectives, pose significant challenges to extended target tracking (ETT). However, a general ETT framework that balances shape estimation accuracy and model complexity is still lacking. Thus, based on Gaussian process theory, this study investigates the adaptive adjustment of measurement model according to the target’s shape information distribution, and proposes a measurement-driven ETT algorithm. The proposed algorithm utilizes the distribution characteristics of real-time measurement density to capture the target’s shape information, and designs a strategy to dynamically adjust the number and positions of basis points, which more effectively represents the complex target shape. Simulation results show that the proposed algorithm significantly improves the estimation accuracy of the extended target by only using 1/3 of the number of basis points compared to existing algorithms, providing an efficient solution for ETT to balance the trade-off between accuracy and complexity.