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<p>Under low-altitude economy, the trajectory prediction of low-altitude aircrafts is critical. However, existing algorithms have key limitations mainly from strong target motion uncertainty and poor adaptability, failing to meet complex scenario precision requirements. To address the unmanned aerial vehicle (UAV) trajectory prediction complexity/diversity in low-altitude scenarios, this paper builds a UAV mission scenario model and designs a progressive learning algorithm based on joint situation data. By integrating long short-term memory and grey prediction, it realizes a real-time accurate analysis of UAV trajectories via depth perception and situation analysis.</p>

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Progressive Learning-Based Trajectory Prediction for Low-Altitude Aircrafts

  • 期刊论文

  • 发表日期：

    2025年12月15日

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  • Hao Zhang,
  • Yuzhi Zhou,
  • Bing Liu,
  • Guoliang Liu,
  • Yunze Cai

摘要

Under low-altitude economy, the trajectory prediction of low-altitude aircrafts is critical. However, existing algorithms have key limitations mainly from strong target motion uncertainty and poor adaptability, failing to meet complex scenario precision requirements. To address the unmanned aerial vehicle (UAV) trajectory prediction complexity/diversity in low-altitude scenarios, this paper builds a UAV mission scenario model and designs a progressive learning algorithm based on joint situation data. By integrating long short-term memory and grey prediction, it realizes a real-time accurate analysis of UAV trajectories via depth perception and situation analysis.