Vision-Based Autonomous MM-Wave Reflector Using ArUco-Driven Angle-of-Arrival Estimation
This addresses the problem of resilient mmWave connectivity for military and civilian operations in urban or infrastructure-limited environments, representing a novel method for a known bottleneck.
The paper tackled the challenge of reliable millimeter-wave communication in non-line-of-sight conditions by developing a vision-aided autonomous reflector system that dynamically steers signal reflections, achieving a 23 dB average gain in received signal strength and an 0.89 probability of maintaining signal reception above a target threshold in indoor tests.
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.