基于卷积神经网络的无人机检测和分类使用叠加的频率调制连续波 (FMCW) 范围多普勒图像
Seung-Kyu Han1, Joo-Hyun Lee2, Young-Ho Jung3
1School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Republic of Korea.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究引入了一种使用卷积神经网络 (CNN) 和雷达数据的新无人机检测方法. 这种方法提高了小型或远距离无人机的准确性,优于传统技术.
科学领域:
- 雷达系统工程 雷达系统工程
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 现有的无人机检测方法由于信号减弱和微弱的微多普勒信号 (MDS) 而与小或遥远的目标作斗争.
- 当前技术的局限性需要先进的解决方案,以可靠地识别无人机.
研究的目的:
- 提出一种使用卷积神经网络 (CNNs) 和频率调制连续波 (FMCW) 雷达的新型无人机检测方法.
- 为了克服与传统基于微多普勒签名 (MDS) 的方法相关的性能降低问题.
主要方法:
- 使用从FMCW雷达生成的距离多普勒地图图像.
- 将多个时间序列范围多普勒图像叠加到一个图像中.
- 使用卷积神经网络 (CNN) 进行无人机检测和分类.
主要成果:
- 在无人机检测准确度方面表现出显著的性能改善.
- 与传统的无人机检测方法相比,实现了更高的准确性.
- 使用三种不同尺寸的无人机的实验数据验证了该方法.
结论:
- 提出的基于CNN的方法有效地提高了无人机检测的准确性,特别是在具有挑战性的场景中.
- 这种新的方法为传统方法提供了强大的替代方案,解决了小型和远距离无人机的局限性.
- 该技术显示出改善监控和安全应用的前景.
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