使用毫米波雷达飞行无人机的分类:在噪音条件下对算法的比较分析
Mauro Larrat1, Claudomiro Sales1
1Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Rua Augusto Corrêa, 01 Guamá, CEP, Belém 66075-110, PA, Brazil.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究利用雷达数据的机器学习增强了无人机检测. 多式变压器模型提高了准确性,特别是在杂的条件下,超过了标准算法.
科学领域:
- 雷达信号处理 雷达信号处理
- 机器学习应用 机器学习应用
- 无人驾驶飞行器 (UAV) 探测系统
背景情况:
- 雷达系统对于检测无人机等物体至关重要.
- 环境噪音对雷达检测算法的性能产生重大影响.
- 现有的机器学习模型面临着在各种噪音条件下强大的无人机识别方面的挑战.
研究的目的:
- 评估和基准机器学习算法用于无人机检测使用60GHz毫米波雷达数据.
- 调查不同算法的针对不同类型噪音的稳定性.
- 为提高无人机和鸟类检测准确性提出一个改进的模型.
主要方法:
- 从生物鸟和两个无人机 (DJI Mavic,DJI Phantom 3 Pro) 收集了雷达数据.
- 基准长期短期记忆 (LSTM),门式递归单元 (GRU),卷积神经网络 (Conv1D) 和变压器算法.
- 引入了一台多式变压器,与振幅和相位数据一起结合了统计特征 (斜率,曲率).
主要成果:
- 变压器显示高精度,但在特定噪声类型 (如帕雷托噪声) 中存在弱点.
- 拟议的多式变压器证明了检测准确度的提高,特别是在具有挑战性的噪声条件下.
- 结果强调了噪音的关键作用和多式联络数据呈现的优势.
结论:
- 机器学习算法,特别是像变形机这样的先进算法,对于基于雷达的无人机检测是有效的.
- 用统计特征 (多式变压器) 增强数据显著提高了算法对噪声的稳定性.
- 这项研究为雷达检测系统提供了一个基准,并对抗噪音的方法提供了洞察力.
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