Scholarlink

<p>Short-term data loss occurs when mobile platforms track unmanned aerial vehicle (UAV) targets, severely impairing the continuity and stability of electro-optical (EO) tracking. A UAV trajectory prediction method integrating flight-state recognition and an attention mechanism is proposed for short-term data-loss scenarios. Based on UAV trajectory data collected by EO tracking systems, an improved support vector machine (SVM) is adopted to identify UAV flight states. A joint architecture combining convolutional feature extraction units and long short-term memory (LSTM) networks is established to mine temporal trajectory features, with an adaptive attention mechanism introduced to strengthen key feature learning. Experimental results show that the improved SVM achieves a recognition accuracy of no less than 90% for all basic flight states. Under 2 s of observation loss, the proposed method outperforms LSTM and Interacting Multiple Model Extended Kalman Filter (IMM-EKF) in root mean square error (RMSE) for turning flight prediction; its accuracy for non-turning states matches Temporal Convolutional Network (TCN) and IMM-EKF. Meanwhile, it features fewer parameters and lower computational cost than lightweight TCN. Ablation experiments verify that flight-state recognition and state-guided attention effectively boost trajectory prediction precision. Extended tests covering multi-gradient velocities, sampling frequencies, prediction horizons and data-loss positions demonstrate the favorable overall performance of the proposed method and define its application scope. In conclusion, the algorithm preliminarily proves its feasibility in addressing short-term data loss for vehicle-mounted EO tracking of low-speed mildly maneuverable UAVs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Low-speed UAV trajectory prediction under short-term data loss: a preliminary feasibility study for vehicle-mounted electro-optical tracking

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月9日

查看全文
  • Xiushuo Wang,
  • Wenxiu Li,
  • Zhaobing Chen,
  • Letang Xue,
  • Zhaolong Wu,
  • Xiangru Ding,
  • Lihua Cao

摘要

Short-term data loss occurs when mobile platforms track unmanned aerial vehicle (UAV) targets, severely impairing the continuity and stability of electro-optical (EO) tracking. A UAV trajectory prediction method integrating flight-state recognition and an attention mechanism is proposed for short-term data-loss scenarios. Based on UAV trajectory data collected by EO tracking systems, an improved support vector machine (SVM) is adopted to identify UAV flight states. A joint architecture combining convolutional feature extraction units and long short-term memory (LSTM) networks is established to mine temporal trajectory features, with an adaptive attention mechanism introduced to strengthen key feature learning. Experimental results show that the improved SVM achieves a recognition accuracy of no less than 90% for all basic flight states. Under 2 s of observation loss, the proposed method outperforms LSTM and Interacting Multiple Model Extended Kalman Filter (IMM-EKF) in root mean square error (RMSE) for turning flight prediction; its accuracy for non-turning states matches Temporal Convolutional Network (TCN) and IMM-EKF. Meanwhile, it features fewer parameters and lower computational cost than lightweight TCN. Ablation experiments verify that flight-state recognition and state-guided attention effectively boost trajectory prediction precision. Extended tests covering multi-gradient velocities, sampling frequencies, prediction horizons and data-loss positions demonstrate the favorable overall performance of the proposed method and define its application scope. In conclusion, the algorithm preliminarily proves its feasibility in addressing short-term data loss for vehicle-mounted EO tracking of low-speed mildly maneuverable UAVs.