用视觉识别标记自动调整毫米波反射板，提升非视距通信性能
Vision-Based Autonomous MM-Wave Reflector Using ArUco-Driven Angle-of-Arrival Estimation
- 通过单目摄像头识别ArUco标记，实时估算信号入射角并控制反射板转向
- 60GHz频段下接收信号强度平均提升23dB，90%概率维持在-65dB以上
- 无需GPS或外部基础设施，适合军事或城市复杂环境中的自适应通信
在城市或基础设施有限的环境中，非视距（NLoS）毫米波（mmWave）通信仍面临重大挑战。本文提出一种基于视觉的自主反射系统，通过电机驱动金属板动态调整信号反射方向，以增强mmWave链路性能。系统采用单目摄像头检测盟军发射端与接收端节点上的ArUco标记，估算其入射角，并实时对准反射板实现最优信号重定向。该方法可仅服务具备可见标记的认证目标，降低信号泄露风险。原型基于Raspberry Pi 4与低功耗硬件构建，无需依赖外部基础设施或GPS，可自主运行。60GHz实验结果表明，接收信号强度平均提升23dB，信号维持在-65dB阈值以上的概率达0.89，显著优于静态和无反射基线。结果证明该系统在复杂动态环境中具备强韧且自适应的mmWave连通潜力。
原文摘要 · Abstract (English)
Reliable millimeter-wave (mmWave) communication in non-line-of-sight (NLoS) conditions remains a major challenge for both military and civilian operations, especially in urban or infrastructure-limited environments. This paper presents a vision-aided autonomous reflector system designed to enhance mmWave link performance by dynamically steering signal reflections using a motorized metallic plate. The proposed system leverages a monocular camera to detect ArUco markers on allied transmitter and receiver nodes, estimate their angles of arrival, and align the reflector in real time for optimal signal redirection. This approach enables selective beam coverage by serving only authenticated targets with visible markers and reduces the risk of unintended signal exposure. The designed prototype, built on a Raspberry Pi 4 and low-power hardware, operates autonomously without reliance on external infrastructure or GPS. Experimental results at 60\,GHz demonstrate a 23\,dB average gain in received signal strength and an 0.89 probability of maintaining signal reception above a target threshold of -65 dB in an indoor environment, far exceeding the static and no-reflector baselines. These results demonstrate the system's potential for resilient and adaptive mmWave connectivity in complex and dynamic environments.
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