基于同位体估计的时空频谱预测,用于随意飞行路径的无人机通信.
Shan Luo1, Wenjun Zhou2, Lifan Wu2
1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, 611731, China. luoshan@uestc.edu.cn.
Scientific reports
|July 11, 2025
概括
无人驾驶飞行器 (UAV) 需要频谱预测才能有效共享. 一种新方法使用同位点理论 (HT) 来估计历史数据,使无人机在任意飞行期间能够准确预测无人机的频谱.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 航空航天工程 航空航天工程
背景情况:
- 无人机 (UAV) 的快速增长导致频谱稀缺.
- 现有的频谱预测方法在移动无人机的实时数据限制方面扎.
- 需要先进的预测模型,能够处理动态环境和有限的历史数据.
研究的目的:
- 为无人机在任意飞行过程中引入一种新的时空频谱预测方法.
- 为应对在未来地点估计历史频谱数据的挑战.
- 开发一种方法,克服频谱数据中的非静止性和相关性问题.
主要方法:
- 同位点理论 (HT) 从两个对象扩展到多个对象.
- 使用从HT边界条件和模型参数中得出的同位素映射来估计历史数据.
- 通过使用HT估计数据的隐藏马尔科夫模型 (HMM) 进行频谱预测,称为多个对象HT-HMM (MOHT-HMM).
主要成果:
- MOHT-HMM方法有效地估计动态无人机轨迹的历史频谱数据.
- 该方法在没有先前历史数据的情况下成功预测下一个位置的频谱状态.
- 使用真实民用航空数据的实验验证表明预测的准确性很高.
结论:
- 在任意飞行场景中,MOHT-HMM为实时无人机频谱预测提供了有效的解决方案.
- 同位体理论是动态系统中估计缺失或未来数据的可行工具.
- 拟议的方法增强了频谱共享能力,用于不断增长的无人机生态系统.
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