使用多天线微多普勒雷达检测城市环境中的小型无人机系统
Detection of sUAS in Urban Environments using Multi-Antenna Micro-Doppler Radar
浏览论文内容
中文总结 AI 辅助
研究城市环境中sUAS检测难题，提出基于连续波多输入多输出雷达和深度学习模型的方法，利用旋翼叶片微多普勒特征的谱相关密度作输入，实验证实在无直接视距条件下检测准确率达86.11% ，可行且有效。
中文摘要 AI 辅助
在现代国防中，小型无人机系统（sUAS）的传感与早期检测至关重要。在密集城市和室内环境中，由于多径密集、衰落、低空飞行和非视距（NLOS）射频传播，检测极具挑战性。本文提出一种连续波多输入多输出雷达及用于利用NLOS信号检测sUAS的深度学习模型。雷达工作于2.47GHz，来自旋翼叶片旋转微多普勒特征的谱相关密度用作深度学习模型输入。实验结果表明，在包含五种无人机类型的数据集中总体检测准确率达86.11%，证实了在无直接视距条件的密集城市环境中检测sUAS的可行性。
英文摘要
Sensing and early detection of small unmanned aerial systems (sUAS) are critically important in modern-day defense. In dense urban and indoor environments, detection becomes extremely challenging due to dense multipath, fading, low-altitude flight, and non-line-of-sight (NLOS) radio-frequency propagation. This paper presents a continuous-wave multiple-input multiple-output radar and a deep learning model for sUAS detection using NLOS signals. The radar operates at 2.47 GHz, and spectral correlation densities derived from rotational micro-Doppler signatures from the rotor blades are used as inputs to the deep learning model. Experimental results demonstrate an overall detection accuracy of $86.11\%$ across a dataset of five drone types, confirming the feasibility of sUAS detection in dense urban environments without direct line-of-sight conditions.
